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DOE OSTI2020

Assessing the sensitivity and repeatability of permanganate oxidizable carbon as a soil health metric: An interlab comparison across soils

Soil organic matter is central to the soil health framework. Therefore, reliable indicators of Soil organic matter is central to the soil health framework. Therefore, reliable indicators of changes in soil organic matter are essential to inform land management decisions. Permanganate oxidizable carbon (PDXC), an emerging soil health indicator, has shown promise for being sensitive to soil management. However, strict standardization is required for widespread implementation in research and commercial contexts. Here, we used 36 soils-three from each of the 12 USDA soil orders-to determine the effects of sieve size and soil mass of analysis on PDXC results. Using replicated measurements across 12 labs in the US and the EU (n = 7951 samples), we quantified the relative importance of 1) variation between labs, 2) variation within labs, 3) effect soil mass, and 4) effect of soil sieve size on the repeatability of PDXC. We found a wide range of overall variability in PDXC values across labs (0.03 to 171.8%; mean = 13.4%), and much of this variability was attributable to within-lab variation (median = 6.5%) independently of soil mass or sieve size. Greater soil mass (2.5 g) decreased absolute PDXC values by a mean of 177 mg kg -1 soil and decreased analytical variability by 6.5%. For soils with organic carbon (SOC) >10%, greater soil mass (2.5 g) resulted in more frequent PDXC values above the limit of detection whereas the lower soil mass (0.75 g) resulted in PDXC values below the limit of detection for SOC contents <5%. A finer sieve size increased absolute values of PDXC by 124 mg kg -1 while decreasing the analytical variability by 1.8%. In general, soils with greater SOC contents had lower analytical variability. These results point to potential standardizations of the PDXC protocol that can decrease the variability of the metric. We recommend that the PDXC protocol be standardized to use 2.5 g for soils <10% SOC. Sieve size was a relatively small contributor to analytical variability and therefore we recommend that this decision be tailored to the study purpose. Tradeoffs associated with these standardizations can be mitigated, ultimately providing guidance on how to standardize PDXC for routine analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Abstract Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

High temperature design methods rely on constitutive models for inelastic deformation and failure typically calibrated against the mean of experimental data without considering the associated scatter. Variability may arise from the experimental data acquisition process, from heat-to-heat material property variations, or both and need to be accurately captured to predict parameter bounds leading to efficient component design. Applying the Bayesian Markov Chain Monte Carlo (MCMC) method to produce statistical models capturing the underlying uncertainty in the experimental data is an area of ongoing research interest. This work varies aspects of the Bayesian MCMC method and explores their effect on the posterior parameter distributions for a uniaxial elasto-viscoplastic damage model using synthetically generated reference data. From our analysis with the uniaxial inelastic model we determine that an informed prior distribution including different types of test conditions results in more accurate posterior parameter distributions. The parameter posterior distributions, however, do not improve when increasing the number of similar experimental data. Additionally, changing the amount of scatter in the data affects the quality of the posterior distributions, especially for the less sensitive model parameters. Moreover, we perform a sensitivity study of the model parameters against the likelihood function prior to the Bayesian analysis. The results of the sensitivity analysis help to determine the reliability of the posterior distributions and reduce the dimensionality of the problem by fixing the insensitive parameters. The comprehensive study described in this work demonstrates how to efficiently apply the Bayesian MCMC methodology to capture parameter uncertainties in high temperature inelastic material models. Quantifying these uncertainties in inelastic models will improve high temperature engineering design practices and lead to safer, more effective component designs.

42 ENGINEERING↗

DOE OSTI2020

Mixed-strategy approach to band-edge analysis and modeling in semiconductors

In semiconductor physics, the Urbach rule describes an exponential dependence of the UV-Vis absorption coefficient on photon energy at the band edge. The rule is so ubiquitously applied that a single common mechanism, variability in the local potential, is generally considered to be the cause of exponential band edges. Variability in the local potential may be caused by alloy inhomogeneity, structural defects, impurities, thermal excitation, or any other mechanism that interrupts ideal crystallinity and creates disorder. A crucial point of the Urbach rule is its development for band edges which approach linearity when plotted on a logarithmic scale. The rule was not developed in the context of nonlinear band edges with complex fine structure. Even so, this model has been applied extensively to band edges with such features. Using differential analysis and deconvolution, we develop an analytical technique capable of separating the components responsible for the fine structure observed. This approach reduces challenging signal-artifact convolution problems to a simple linear combination. Furthermore, we develop a model that can be applied to the nonlinear band-edge signals extracted. In this work, we show that in the limiting case of linearity, our model is consistent with and produces the same results as the linear Urbach model. Our model is capable of bypassing “tailing” as an indirect measure of the spread in the local potential and gives a direct measure of this quantity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Industry Partnerships & Their Role In Reducing Natural Gas Supply Chain Greenhouse Gas Emissions – Phase 2

This analysis is the product of collaboration between Our Nation’s Energy Future (ONE Future) and the United States (U.S.) Department of Energy (DOE) National Energy Technology Laboratory (NETL). This analysis is an update to Phase 1 of the collaboration between ONE Future and NETL. Phase 1 had three objectives: 1. Calculate a greenhouse gas (GHG) emission profile representative of ONE Future’s supply chain, including methane (CH 4 ) emission rates. 2. Compare ONE Future’s emission profile to the emission profile for the U.S. natural gas supply chain. 3. Evaluate specific emission reduction opportunities. Phase 2 has two key enhancements over Phase 1: 1. The reporting year of the data is updated from 2016 to 2017. 2. The emission profiles and the specific emission reduction opportunities are regionalized for the ONE Future supply chain. The ONE Future supply chain is based on data provided by ONE Future members for all their U.S. onshore assets. ONE Future’s data are mostly representative of their participation in the Greenhouse Gas Reporting Program (GHGRP) administered by the Environmental Protection Agency (EPA) and is supplemented by ONE Future facilities that are not required to report to GHGRP. The U.S. scenario is based on NETL’s life cycle analysis (LCA) of natural gas extraction and power generation, which also uses data from the GHGRP (but does not include non-GHGRP facilities). In addition to the data from these sources, NETL accounted for uncertainty due to data variability, data limitations, and variability in liquids unloading frequency and event duration. This report is a re-issue of the 2020 study (published on July 28, 2020). It revises the device count in the mitigation strategy “pipeline pump-down before maintenance” in the Marginal Abatement Cost (MAC) Analysis section of the report (Section 7). Please see the addendum for more details on this revision and resultant changes. This revision does not change most of our conclusions and recommendations. It only changes the total methane reduced from all the mitigation opportunities (low cost and high cost) from 6.5 Bcf CH 4 /yr to 4.8 Bcf CH 4 /yr.

03 NATURAL GAS↗

DOE OSTI2022

Exploring the Spatial Relationship Between Demographic Indicators and the Built Environment of a City

In addition to global and regional drivers of urbanization, neighborhood development in urban areas across the United States has been shown to be influenced by various local socio-economic factors. These factors, despite varying across socio-economic groups, have large implications regarding a population’s vulnerability to extreme climate events, including heat waves resulting in adverse health impacts. Additionally, the demographics of an urban area can shape its infrastructural characteristics, causing different populations groups to face varying levels of risks and benefits. As a result, the urban morphology and socio-economic characteristics of a city are deeply intertwined; however, their interactions on a finer scale are not yet fully understood. This research aims to better understand the relationships between various socio-economic factors and the built environment of a city, considering variability in building types, and temperature patterns. This research focuses on the city of Las Vegas, NV, and uses spatial data analysis to understand the correlation between of socio-economic characteristics, building morphology, building characteristics, and temperature data to understand the correlation between these various factors. Results of these research shows there is a distinct pattern of clustering of socio-economic characteristics with the city and there is a distinct correlation between age and cost, socio-economic characteristics, and locations of high heat distribution within the city.

Singh, Ridhima↗

DOE OSTI2022

HȮ 2 + HȮ 2 : High level theory and the role of singlet channels

At low temperatures and high pressures, the HȮ 2 + HȮ 2 reaction is an important reaction in combustion. There are significant unresolved discrepancies between prior theoretical and experimental studies of this reaction. It has generally been presumed to occur as an abstraction on the triplet surface to produce H 2 O 2 + 3 O 2 . Here, we employ a combination of high-level electronic structure theory (the ANL0 composite method or multi-reference methods as appropriate), sophisticated transition state theory (vibrationally adiabatic torsions, variational, and variable reaction coordinate), and master equation analyses to predict the thermal kinetics on the H 2 O 4 surface. Notably, this analysis suggests a significant branching to HȮ 3 + ȮH near 1000 K via reaction on the singlet surface. This channel does not appear to have been considered in prior combustion models. HȮ 3 itself is a metastable complex (bound by only 3 kcal mol -1 ) that rapidly dissociates to ȮH + 3 O 2 . Thus, the net reaction for this channel, HȮ 2 + HȮ 2 → ȮH + ȮH + O 2 , converts two low reactivity HȮ 2 radicals into two highly reactive ȮH radicals. The ramifications of these newly derived rate expressions are highlighted through kinetic modeling studies of H 2 , CH 3 OH, $\textit{n}$-heptane, and isooctane oxidation; all at the high pressures of relevance to combustion devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Evaluation of sustainable waste management: An analysis of techno-economic and life cycle assessments of municipal solid waste sorting and decontamination

This study evaluates the economic and environmental feasibility of Municipal Solid Waste (MSW) sorting and decontamination technologies across urban, suburban, and rural areas. Using Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), the research assesses cost-effectiveness and environmental impacts, with a focus on cost variability analyzed through Monte Carlo simulations. Findings indicate significant cost differences based on population density: rural areas incur high costs up to $$764/ton due to low waste volumes and limited infrastructure, whereas suburban and urban areas have more feasible costs ranging from $36.3 to $142.5/ton. Environmental impacts also vary, with greenhouse gas emissions at 171 kg CO 2 eq/ton for copy paper and 118.6 kg CO 2 eq/ton for plastics. PM2.5 levels are 9.1 g/ton for copy paper and 6.3 g/ton for plastics, with sorting lines being the main contributors. Monte Carlo simulations reveal a 50% probability of costs being below $$102.26/ton for copy paper and $115.8/ton for plastics in suburban settings. Further, the study underscores the importance of customized waste management strategies to improve economic viability and sustainability based on local conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Cooperativity and Metal–Linker Dynamics in Spin Crossover Framework Fe(1,2,3-triazolate) 2

Cooperative interactions are responsible for the useful properties of spin crossover (SCO) materials–large hysteresis windows, critical temperatures near room temperature, and abrupt transitions–with hybrid framework materials exhibiting the greatest cooperativity and hysteresis of all SCO systems. However, little is known about the chemical origin of cooperativity in frameworks. Here, we present a combined experimental–computational approach for identifying the origin of cooperativity in the metal–organic framework (MOF) Fe(1,2,3-triazolate) 2 (Fe(TA) 2 ), which exhibits the largest known hysteresis window of all SCO materials and unusually high transition temperatures, as a roadmap for understanding the manipulation of SCO behavior in general. Variable-temperature vibrational spectroscopy provides evidence that “soft modes” associated with dynamic metal–linker bonding trigger the cooperative SCO transition. Thermodynamic analysis also confirms a cooperativity magnitude much larger than those of other SCO systems, while electron density calculations of Fe(TA) 2 support previous theoretical predictions that large cooperativity arises in materials where SCO produces considerable differences in metal–ligand bond polarities between different spin states. Taken together, this combined experimental–computational study provides a microscopic basis for understanding cooperative magnetism and highlights the important role of dynamic bonding in the functional behavior of framework materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

What CMIP6 Models Tell Us About the Impact of AMOC Variability on the Arctic

In this paper we address the question whether variability in the Atlantic Meridional Overturning Circulation (AMOC) and its associated heat transport at mid-latitudes impacts the Arctic Earth system. To that end, we perform coherence analysis on time series of ocean heat transport, AMOC strength, and Arctic climate metrics across a large number of models from the CMIP6 ensemble. We find that, on multidecadal timescales, the majority of CMIP6 models indeed display a statistically significant relationship between AMOC at mid-latitudes (45 °N) and metrics like Arctic surface air temperatures and sea ice. However, our results do not support the narrative that heat transport anomalies at lower latitudes propagate northward toward the Arctic. Instead, our results confirm that variability in meridional ocean heat transport arises in the subpolar North Atlantic and propagates southward and northward.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Probing exciton diffusion dynamics in photosynthetic supercomplexes via exciton–exciton annihilation

Photosynthesis converts solar energy into chemical energy through coordinated energy transfer between light-harvesting complexes and reaction centers (RCs). Understanding exciton motion, particularly the exciton diffusion length, is essential for optimizing energy efficiency in photosystems. In this work, we combine intensity-cycling transient absorption spectroscopy with kinetic Monte Carlo (kMC) simulation to investigate exciton motion in the C2S2 photosystem II supercomplex of spinach. Using exciton–exciton annihilation, revealed in the fifth-order response, we experimentally estimate an exciton diffusion length of 10.9 nm based on a 3D normal diffusion model, suggesting the ability of excitons to traverse the supercomplex. However, kMC simulations reveal that exciton motion is sub-diffusive because of spatial constraints and the strong RC traps. An anomalous diffusion model analysis of the experimental data yields a diffusion length of 9.7 nm, while the simulated diffusion length is 7.4 nm. The variable exciton residence time across subunits, partly influenced by their connectivity to the trap, indicates inhomogeneous annihilation probability and suggests how plants balance efficient light harvesting with photoprotection. We also explore the influence of specific assumptions in the annihilation simulation, which are challenging to access in more complex environments, such as the thylakoid membrane. Our study provides a framework for studying exciton dynamics using exciton–exciton annihilation, which can be extended to understand the light-harvesting efficiencies of larger, more complex photosynthetic assemblies.

Zhang, Kunyan (ORCID:000000026830409X)↗

DOE OSTI2021

Water Migration and Swelling in Engineered Barrier Materials for Radioactive Waste Disposal

Deep, underground repositories are needed to isolate radioactive waste from the biosphere. Because bentonite is an integral component of many multibarrier repository systems, information on the hydraulic behavior of bentonite is crucial for modeling the long-term viability of such systems. In this paper the hydraulic behavior of bentonite samples was analyzed as a function of aggregate size, and samples were subjected to hydrothermal treatments involving contact with NaCl, KCl, and deionized water. Neutron and X-ray imaging were used to quantify water sorption into packed bentonite samples and bentonite swelling into the water column. The distance between the original clay-water interface and the wetting front was determined as a function of time. Average water uptake exhibited a square-root-of-time dependence in freshly prepared samples, but more variable rates were observed for samples previously in contact with water. The radiography was supported by small-angle neutron scattering analysis and ultra-small-angle neutron scattering analysis of aggregate size distributions and by inelastic neutron scattering to understand the physicochemical environment of the sorbed water. Results showed that hydrothermal treatment with KCl had the greatest effect of increased water transport in the bentonite, possibly as a result of the interaction of K + with smectite layers in the clay.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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 OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A Class of Sparse Johnson–Lindenstrauss Transforms and Analysis of their Extreme Singular Values

The Johnson–Lindenstrauss (JL) lemma is a powerful tool for dimensionality reduction in modern algorithm design. The lemma states that any set of high-dimensional points in a Euclidean space can be projected into lower dimensions while approximately preserving pairwise Euclidean distances. Random matrices satisfying this lemma are called JL transforms (JLTs). Inspired by existing $s$-hashing JLTs with exactly $s$ nonzero elements on each column, the present work introduces an ensemble of sparse matrices encompassing so-called $s$-hashing-like matrices whose expected number of nonzero elements on each column is $s$. The independence of the sub-Gaussian entries of these matrices and the knowledge of their exact distribution play an important role in their analyses. Using properties of independent sub-Gaussian random variables, these matrices are demonstrated to be JLTs, and their smallest nontrivial singular values and largest singular values are estimated nonasymptotically using a technique from geometric functional analysis. As the dimensions of the matrix grow to infinity, these singular values are proved to converge almost surely to fixed quantities (by using the universal Bai–Yin law) and in distribution to the Gaussian orthogonal ensemble Tracy–Widom law after proper rescalings. Understanding the behaviors of extreme singular values is important in general because they are often used to define a measure of stability of matrix algorithms. For example, JLTs were recently used in derivative-free optimization algorithmic frameworks to select random subspaces in which are constructed random models or poll directions to achieve scalability, and hence estimating their smallest singular value in particular helps determine the dimension of these subspaces.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Data Agnostic Feature-Target Analysis & Ranking Machine Learning Pipeline (DAFTAR-ML) v0.1.0

DAFTAR-ML is a specialized machine-learning pipeline that identifies relevant features based on their relationship to a target variable. Many ML pipelines focus solely on prediction, and feature ranking is often absent or lacks robust statistical methods. DAFTAR-ML performs its tasks with this outcome in mind. Model training is robust, using nested cross-validation and hyperparameter tuning. Instead of relying on native feature-importance scores, it employs SHAP (SHapley Additive exPlanations) to quantify feature importance. The pipeline also produces comprehensive results, including publication-quality visualizations.

Melie, Tina [Lawrence Berkeley National Laboratory↗

DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Integrating Cambium Marginal Costs into Electric Sector Decisions: Opportunities to Integrate Cambium Marginal Cost Data into Berkeley Lab Analysis and Technical Assistance

NREL’s Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S. Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In this report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗