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DOE OSTI2023

Cyberguardians and STEM Warriors (Final Technical Report (FTR))

In the past decade, solar power, with and without energy storage has become the fastest growing source of energy generation in the world. In the U.S., solar employment more than doubled from 105,145 jobs in 2011 to 255,037 jobs in 2021, four times faster than the U.S. job growth rate overall. These factors, combined with technology advancements, creates a skills gap that puts tremendous stress on society to deliver the workers to fill the open job requisitions. The Cyberguardians and STEM Warriors project (Cyberguardians) was designed to address the trained-worker shortage in the energy industry in three ways: 1) by developing educational curriculum that addresses DER technology changes; 2) by delivering curriculum to prospective workers, including military veterans and their families, via universities, community colleges, and vocational training outlets; and 3) introducing individuals who have completed training to employers that can hire them. Cyberguardians exceeded its curriculum goals by producing 27 academic units of university-accredited material (12 total courses) covering energy fundamentals, smart inverters, Distributed Energy Resource (DER) data communication, cybersecurity, standardization, certification, data analytics, and IEEE 1547 standard topics. The North American Board of Certified Energy Practitioners (NABCEP) also accredited the material for use in their credential program. Seven instructors were recruited and trained, and six academic institutions (University of California San Diego, State University of New York, North Carolina State University, Harper Community College, Green Village Academy, and the SunSpec Alliance) were enlisted, meeting program goals. All course material was published under the Creative Commons license and made available royalty free, thus providing a long-lasting public benefit. The program’s outreach program vastly exceeded program goals and incorporated the efforts of 13 outreach partners (11 of which are veteran focused), an advisory board representing 15 companies, webinars and 10’s of thousands of email messages sent to prospective students and hiring managers. Despite these efforts, the global pandemic depressed anticipated program participation by about a third. Still, a total of 396 students enrolled and 289 completed the courses and were accredited. The job applicant task achieved similar results (111 realized vs a 174 goal) but reported job placement was weaker at (9 realized vs. a 51 goal). The Cyberguardians program fills a critical void for cost-effective, royalty-free curriculum and training pertaining to DER technologies and cybersecurity that prospective energy workers must possess to be effective in the 21 st century. On this basis alone, the investment of taxpayer funds will pay dividends for years to come.

14 SOLAR ENERGY↗

DOE OSTI2024

NSA Site Science: Use of ARM Observations from Northern Alaska to Evaluate and Improve Prediction Capabilities

The Arctic is warming at a rate nearly double that of the rest of the planet, leading to profound changes in atmospheric, oceanic, and ice processes. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility has played a significant role in Arctic research, operating observatories in Alaska's North Slope for over 25 years. These observatories provide a rich, and wide-reaching dataset that offers insight into atmospheric processes in northern Alaska. This report details the results of a nine-year research project (2015–2024) supported by the DOE Atmospheric Systems Research (ASR) program, that leverages data from ARM’s deployment of observing facilities at Utqiaġvik (known as the North Slope of Alaska, or NSA, site) and Oliktok Point, Alaska. The project was conducted in two phases: - Phase 1 (2015–2019): Focused on understanding key atmospheric processes at Oliktok Point, including cloud formation, high-latitude precipitation, aerosol-cloud interactions, and cloud properties. - Phase 2 (2019–2024): Extended the research to the broader North Slope region, using data from both Oliktok Point and Utqiaġvik. Topics explored included surface energy budgets, atmospheric stability, ice nucleation processes, and microphysics in Arctic clouds. The project resulted in numerous research products, including 50 peer-reviewed publications and dissertations, 169 presentations, and 10 data products. These products cover a variety of topics, including: - Cloud Macro- and Microphysical Properties: Arctic clouds play a crucial role in energy transfer, and accurate representation in models is critical. The study explored cloud transitions, ice crystal shapes, and dual-wavelength radar data to understand ice crystal habits and size distributions. - Aerosol Properties and Processes: The team examined aerosol sources in the Arctic, including industrial emissions and natural sources. Observations showed significant spatial gradients in aerosol concentrations due to human activities and wildfire smoke. The influence of aerosols on cloud formation and the surface energy budget was also assessed. - Aerosol-Cloud Interactions: Research revealed that aerosols might suppress cloud ice production, affecting cloud radiative forcing and precipitation. The impact of local industrial emissions on cloud properties was also investigated. - Contextualizing the North Slope of Alaska in the context of the broader Arctic: To understand broader trends, the project evaluated large-scale circulation patterns and the influence of weather systems on the Arctic. Studies indicated that large-scale processes play a significant role in temperature patterns and the timing of snowmelt. - Advancing ARM Observational and Modeling Capabilities: The project developed new radar data products and advanced measurement techniques, including clutter mitigation and drizzle detection. Uncrewed aerial systems (UAS) and tethered balloon systems (TBS) were deployed to gather detailed atmospheric data. Additionally, the project supported 10 early career scientists, providing training and mentorship to undergraduate interns, graduate students, postdoctoral researchers, and early career researchers. These efforts contributed to the advancement of ARM research capabilities and fostered a new generation of scientists skilled in Arctic atmospheric research. Ultimately, this ASR-supported project has provided valuable insights into Arctic atmospheric processes and their broader climate implications. Recommendations for future work include continuing support for long-term observing at Arctic locations to foster additional research, further exploration of aerosol-cloud interactions and the potential impacts of enhanced industrialization of the Arctic, and expanded use of uncrewed systems to gather data in this remote and harsh environment. Additionally, the data products developed by this work, and the data products developed through the ARM infrastructure, leave a treasure-trove of additional information that should be explored for many years to come to gain additional insight into physical processes in the Arctic atmosphere that drive the rapid changes occurring in at high latitudes and their global impact.

58 GEOSCIENCES↗

DOE OSTI2022

A Survey on Sustainable Software Ecosystems to Support Experimental and Observational Science at Oak Ridge National Laboratory

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. The study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and ii) data management requirements for data-driven science using artificial intelligence.

Bernholdt, David↗

DOE OSTI2022

NNSA Minority Serving Institute Partnership Program (MSIPP)— Advanced Synergistic Program for Indigenous Research in Engineering (ASPIRE) (FY22 Q1 Progress Report)

In 2016, the National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) targeting Tribal Colleges and Universities (TCUs) to offer programs that will prepare students for technical careers in NNSA’s laboratories and production plants. The MSIPP consortium’s approach is as follows: 1) align investments at the college and university level to develop a curriculum and workforce needed to support NNSA’s nuclear weapon enterprise mission, and 2) to enhance research and education at under-represented colleges and universities. The first TCU consortium that MSIPP launched was known as the Advanced Manufacturing Network Initiative (AMNI) whose purpose was to develop additive manufacturing (AM) learning opportunities. The AMNI consortium consisted of Bay Mills Community College, Cankdeska Cikana Community College, Navajo Tech University, Salish Kootenai Community College, Turtle Mountain Community College, and United Tribes Technical College. In 2016, the American Indian Higher Education Consortium (AIHEC), the AMNI consortium and the Southwestern Indian Polytechnic Institute (SIPI), in collaboration with Sandia National Labs, using a grant by NNSA hosted the first TCU Advanced Manufacturing Technology Summer Institute (TCU AMTSI). The AMNI consortium will officially end Sept. 2022. However, building on the successes of AMNI, in FY22 NNSA’s MSIPP launched three additional consortiums: (1) the Indigenous Mutual Partnership to Advanced Cybersecurity Technology (IMPACT), which focuses on STEM and cybersecurity, (2) the Advanced Synergistic Program for Indigenous Research in Engineering (ASPIRE), which focuses on STEM and the electrical and mechanical engineering skills set needed for renewable and distributed energy systems, and (3) the Partnership for Advanced Manufacturing Education and Research (PAMER), which focuses on developing and maintaining a sustainable pathway for a highly trained, next-generation additive manufacturing workforce and a corresponding community of subject matter experts for NNSA enterprises. The following report summarizes the status update during this quarter for the ASPIRE program.

42 ENGINEERING↗

DOE OSTI2024

A Collaborative Industrial Assessment Center (IAC) for Expanded Outreach within the Southeast

This GA-FL IAC had impacts on workforce development and economic impact for regional small- and medium-sized enterprises. Qualified faculty and campus staff educated and trained cohorts of students regarding state-of-the-art industrial assessments. Students gained hands-on experiences in learning about manufacturing processes, energy systems, developing assessment recommendations/calculations, and client interaction. These skills were complemented by the incorporation of IAC themes into some course curricula. As far as economic impact, the center conducted 66 assessments over 5 years primarily in Georgia and Florida. These represented hundreds of thousands of dollars in potential annual resource savings to the clients and region.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Beams of polarized hadrons at EIC: Action plan to address acute challenges

In the recent years, more and more groups in the field of polarization instrumentation and technology have disappeared, taking their expertise in the experimental technologies with them. When the RHIC was conceived, there were numerous experimental and theoretical groups around the world from which experts could be recruited to build new instruments, powerful sources, specialized detector systems, investigate new approaches and concepts, and much more. We should recognize that we presently are in a critical situation regarding the availability of skilled individuals to provide and service the required instruments to make the EIC a success. We need to rejuvenate the field of polarization technology, especially for hadrons, and significantly expand education and training efforts. The document highlights a number of key areas that require attention. Three scientists/engineers/postdocs and at least one or two PhD students should be sought immediately to start building a group capable of tackling the future tasks and challenges in hadron polarimetry at the EIC.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Characterization and application of a high-resolution microfluidic device in atmospheric ice nucleation research and integrated science teaching

Ambient ice-nucleating particles (INPs) represent an important subset of aerosol particles that facilitate heterogeneous formation of ice crystals in the atmosphere, which can impact Earth system and energy. In this work, we developed an affordable high-resolution platform to replicate atmospheric immersion freezing through the unique capabilities of microfluidics to measure INP concentration of known ice nucleation active materials, as well as high-latitude soil samples. The new instrument produced comparable and extended freezing efficiency data for a multitude of samples over the temperature range of -5 to -35 °C as compared to an existing freezing assay within known uncertainties. Moreover, the new assay was integrated into science teaching at a primarily undergraduate and Hispanic-serving institute, contributing to fostering a deeper understanding of ice nucleation processes among college students, instilling their enthusiasm for science, and addressing the critical need for a well-informed citizenry. Student research assistants were involved in both research and pedagogical development processes. Three students from our team visited the Pacific Northwest National Laboratory to help characterize physicochemical properties of our test samples and to test modeling of atmospheric ice nucleation using a box model as well as a global climate model (E3SM) for the regional-scale conditions over the North Slope of Alaska (NSA) and Southern Great Plains (SGP). We also assessed student learning experience via hands-on training, research processes, and development of educational materials. On pre- and post-participation surveys and in exit interviews, students reported personal growth as advancements in breadth of experience, research-related skill sets, and regarding their educational and career goals. They also affirmed that the methods enacted incorporated the NASA Minority University Research and Education Program (MUREP) STEM Engagement Best Practices.

58 GEOSCIENCES↗

DOE OSTI2023

Experiential findings for sustainable software ecosystems to support experimental and observational science

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. Here, the study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: (i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and (ii) data management requirements for data-driven science using artificial intelligence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s −1 ) and damaging (≥25.7 m s −1 ) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗

DOE OSTI2024

Nanoporous Materials Genome Center Final Technical Report

Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the DOE Office of Science Basic Energy Sciences (BES) mission and objectives. The Nanoporous Materials Genome Center (NMGC) has developed exascale-ready software, computational/theoretical chemistry methods, and data-driven science approaches that enable (i) the de-novo design of functional NPMs for chemical separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical adsorbent structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs including hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces. A pivotal part of the NMGC project has been a tight collaboration between leading experimental groups for synthesis and characterization of NPMs and of computational groups that allowed for iterative feedback. The NMGC project has resulted in the publication of more than 290 research and review articles including more than 60 publications in high-impact journals and more than 15 journal covers. NMGC publications have already received more than 20,000 citations (with more than 3,000 citations per year in 2021, 2022, and 2023) and contribute to an h-index of more than 72. The NMGC award has supported collaborative research involving 28 research groups and contributed to the training of more than 40 postdocs, more than 60 graduate students, and more than 20 undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)

Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.

Advanced Manufacturing↗

DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

US Department of Energy Better Plants Program’s Approach to Manufacturing Workforce Development

Multiple studies suggest that by 2030, millions of jobs may go unfilled because of a significant skills gap between the current manufacturing workforce and the workforce needed to support the shift toward advanced and sustainable manufacturing. According to US manufacturers, finding the right talent is more difficult now than in previous years. Developing a well-trained workforce will play a critical role in transforming the manufacturing sector into a net-zero contributor of greenhouse gas emissions by 2050. To help reduce energy, water, waste, and greenhouse gas emissions as well as build a strong, sustainable manufacturing workforce, the US Department of Energy created the voluntary Better Plants program in 2011. This study discusses the program’s various workforce development activities, including in-plant trainings, virtual in-plant trainings, and in-person bootcamps. The target audiences, training objectives, and topics designed to upskill workers for a low-carbon manufacturing future are described. Feedback that was collected (e.g., new training topics, certifications, hands-on activities) while working with more than 270 US manufacturers for the past 12 years on workforce development are discussed. Finally, some strategies are proposed to address the feedback.

Guo, Wei↗

DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗

DOE OSTI2021

A convergence research perspective on graduate education for sustainable urban systems science

Sustainable urban systems (SUS) science is a new science integrating work across established and emerging disciplines, using diverse methods, and addressing issues at local, regional, national, and global scales. Advancing SUS requires the next generation of scholars and practitioners to excel at synthesis across disciplines and possess the skills to innovate in the realms of research, policy, and stakeholder engagement. Here, we outline key tenets of graduate education in SUS, informed by historical and global perspectives. The sketch is an invitation to discuss how graduates in SUS should be trained to engage with the challenges and opportunities presented by continuing urbanization.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗