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DOE OSTI2022

New Improved Horizontal Electropolishing System for SRF Cavities

The best performance of niobium SRF accelerating cavities is obtained with surfaces smoothed with electropolishing chemical finishing. Jefferson Lab has recently specified, procured, installed, and commissioned a new versatile production electropolishing (EP) tool. Experience with EP research and operations at JLab as well as vendor interactions and experience guided development of the system specification. Detailed design and fabrication was awarded by contract to Semiconductor Process Equipment Corporation (SPEC). The delivered system was integrated into the JLab chemroom infrastructure and commissioned in 2020. The new EP tool provides much improved heat exchange from the circulating H₂SO₄/HF electrolyte and also the cavity via variable temperature external cooling water flow, resulting in quite uniform cavity wall temperature control and thus improved removal uniformity. With the JLab infrastructure, stabilized process temperature as low as 5 C is available. We describe the system and illustrate operational modes in this contribution.

Reece, C. E.↗

DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Dataset_for_Molecular_Motion_Below_the_Glass_Transition_A_Solid-State_NMR_Study_of_Siloxane_Polymer_Dynamics Study

This dataset contains solid-state 1H and 13C NMR relaxometry data, differential scanning calorimetry (DSC) data, and size exclusion chromatography (SEC/GPC) data supporting the study of sub-glass-transition (sub-Tg) molecular dynamics in a composition- and sequence-controlled series of diphenyl-substituted polysiloxanes (PDMS, 14Ph, 33Ph, 50Ph, 67Ph, and 100Ph; 0–100% diphenylsiloxane content by mole).All solid-state NMR data were acquired on a 200 MHz Bruker Avance III HD spectrometer using a static 7 mm HX probe or a 4 mm HX probe under 4 kHz magic-angle spinning. Raw Bruker TopSpin experiment folders are included for: (1) variable-temperature 1H lineshape measurements used to determine linewidth (FWHM) as a function of temperature across the glass transition; (2) 1H T1 (saturation recovery with solid-echo detection), probing nanosecond-scale dynamics near the 1H Larmor frequency; (3) 1H T1rho (direct spin-lock, 62.5 kHz), probing microsecond-scale segmental dynamics; (4) 13C-detected Lee–Goldburg cross-polarization 1H T1rho (LGCPH T1rho) for 33Ph and 50Ph, resolving aromatic and aliphatic proton environments; and (5) 13C T1 relaxation for 33Ph and 50Ph. Differential scanning calorimetry data (TA Instruments DSC 25, −150 to +120 °C, up to +300 °C for 100Ph, 10 °C/min) are included for all six compositions and support the glass-transition temperatures in Table 1 and Figure 1. Size exclusion chromatography data (Agilent 1200 Series, PL-Gel 300 mixed-C column, THF mobile phase, polystyrene calibration standards) are included for the three synthesized copolymers (33Ph, 50Ph, 67Ph) and support the number-average molecular weights in Table 1. Processed data include per-composition relaxation-time summaries (Excel), curve-fitting and Bloembergen-Purcell-Pound (BPP) model analysis notebooks (Jupyter/Python), and Igor Pro (.pxp) master files used to generate the manuscript's figures.

Bloembergen-Purcell-Pound theory↗

DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗

DOE OSTI2026

CMIP7 Data Request: atmosphere priorities and opportunities

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data unlocking key research avenues in atmospheric science and provides justification for the resources needed to produce this data. Topics within the CMIP7 Atmosphere Theme centre around processes and feedbacks in atmospheric science such as clouds, aerosols and atmospheric chemistry, atmospheric circulation, temperature variability and extremes, radiative forcings, and Earth system model evaluation. These topics are summarised in this paper as scientific “opportunities” which will be realised through CMIP7 experiments and Earth system model outputs. These opportunities were submitted by a thematic group of atmospheric science community representatives combined with an extended consultation process. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making, including supporting the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). As an author group, we also reflect on the process used to collate this data request and make recommendations to future CMIP governance on implementing a consultation on this scale in the future.

58 GEOSCIENCES↗

DOE OSTI2022

JHU Baltimore iButton data

Temperature and Relative Humidity data from a low cost sensor ("iButton") network deployed in Baltimore in summer 2015, 2016, 2017, 2021, and 2022. Initial deployment described in Scott, A., Zaitchik, B., Waugh, D., O'Meara, K. 2017. Intra-Urban temperature variability in Baltimore. Journal of Applied Meteorology and Climatology, 56(1): 159-171. doi: 10.1175/JAMC-D-16-0232.1

applied meteorology↗

DOE OSTI2023

Projections of Hourly Meteorology by Balancing Authority Based on the IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulations

This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for 54 Balancing Authorities (BAs) in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). To go from the TGW data to these BA-level aggregated data we first averaged the raw gridded data by county in the United States. That intermediate data step is also stored in MSD-LIVE (https://doi.org/10.57931/1960548). We then population-weight the county-level hourly data in order to create population-weighted meteorology time series for each BA. Historical populations are from the United States Census Bureau and the evolving future populations are based on Shared Socioeconomic Pathways (SSPs) 3 and 5. The four climate scenarios crossed with the two SSPs yield eight different future projections for each BA: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotterssp3, rcp85hotterssp5. For the historical period and each of the eight future scenarios the dataset has hourly estimates of the population-weighted average of five meteorological variables: Temperature, specific humidity, shortwave radiation, longwave radiation, and wind speed. All times are in Coordinated Universal Time (UTC). The code to go from the raw TGW data to county-level and then BA-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.

Balancing Authority↗

DOE OSTI2021

Switched low-dropout voltage regulator

High-resolution switched digital regulators are disclosed having fast cross corner and variable temperature response, with constrained ripple. The strength of the power transistors utilized by the regulator are adjusted to control the current delivered to the load. The regulators utilize a slow control loop in parallel with a primary fast switching loop. The slow loop uses the switching signal of the primary loop to estimate the load current and set the power transistor size accordingly.

Kudva, Sudhir Shrikantha↗

DOE OSTI2022

Multi-Objective Cycle Optimization of an Integrally Geared Waste Heat Recovery Unit for a Combined Cycle Power System

This paper has presented the cycle design and optimization details for a sCO2-based WHRS targeting the Solar Turbines Titan 130. The PreheatSR cycle layout was chosen to effectively address the issue of acid dew point corrosion and ensure high system performance is not significantly impacted by use of alternative fuels. The optimization process discussed uses a multi-objective optimization to discover a series of optimal cycle configurations given ambient temperature variability for a chosen site location while considering the initial capital cost of the cycle components. Cycle models built that incorporated off-design methods for the heat exchangers and turbomachinery allowed for the investigation of cycle operation that maximizes power output for individual cycle conditions. The resulting Pareto front serves as a guide for how to configure the WHRS cycle for the highest yearly energy extracted for a given investment.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Understanding Air-Sea Feedbacks to the MJO through Process Evaluation of Observations and Models (Final Report)

The goal of our project was to enhance our understanding of the role of coupling in the simulation of the Madden Julian Oscillation (MJO) in coupled climate models. We developed diagnostics of precipitation and Sea Surface Temperature variability in climate models and investigated how they are related to metrics of MJO simulation performance. We found a relationship between models with a high spatial coherence of precipitation and a good propagation of the MJO through the Maritime Continent region, but no relationship between metrics of precipitation organisation and intensity. Analysis of coupled and uncoupled model simulations from the Sixth Coupled Model Intercomparison Project (CMIP6) shows that coupling systematically improved the MJO simulation in both metrics that we investigated. We analysed the role of background moisture gradients in the MJO simulation performance following Lim et al. (2018) and found that although the model to model variation in moisture gradients to the west of Indonesia was related to model MJO performance, in general no single moisture gradient was as good at explaining a large fraction of MJO performance across the models as compared to a linear combination of those metrics, which was able to explain a significant fraction of the variance in the MJO performance. Those relationships were much stronger for coupled simulations than uncoupled simulations and stronger for those metrics that measured propagation of the MJO across the Maritime Continent than for overall MJO activity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Carrier Dynamics and Photoluminescence in Antimony-Doped CdTe and CdSeTe

Arsenic (As)-doped graded Cd(Se,Te) photovoltaics have achieved record efficiency of 22.4%, but further increases in efficiency have been limited by doping activation of only ~1% and radiative voltage losses of ~100 meV. Antimony (Sb) can also act as a p-type dopant of Cd(Se,Te) but has received relatively little attention. We measured carrier dynamics and photoluminescence of Sb-doped CdTe and CdSeTe films grown by vapor-transport deposition, with dopant activation up to ~20% Time resolved terahertz (TRTS) and photoluminescence (TRPL) data were fit by simulating the semiconductor equations, revealing bulk lifetimes of >20 ns in CdTe:Sb and >65 ns in CdSeTe:Sb and surface recombination velocities <1000 cm/s at alumina interfaces. We also qualitatively compare transients and PL spectra of doped and undoped films, with no apparent variation associated with hole concentrations up to 1015 cm-3. Preliminary variable temperature steady state photoluminescence (VT-SSPL) indicates dominant band-gap emission in CdSeTe:Sb and no evidence of potential fluctuations with Sb-doping up to this hole concentration.

14 SOLAR ENERGY↗

DOE OSTI2020

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Automated point dendrometer, soil moisture and temperature, and meteorological variables datasets, Oct 2024 – Nov 2025, G.A. Pearson Natural Area, Flagstaff, AZ, USA

This data package includes parsed, cleaned, and calibrated data from 48 TOMST automated point dendrometers, 48 TOMST 15 cm soil moisture sensors, and 12 TOMST 30 cm soil moisture sensors. The point dendrometers were cleaned with the “dendRoAnalyst” package in RStudio. The soil sensors were cleaned and calibrated for volumetric water content (VWC) with the “myClim” package in RStudio using the soil texture of the site (sandy clay loam). Additionally, this data package also includes raw data from 2 METER weather stations. Dendrometers and soil sensors have both their sensor ID, as well as the ID for the specific tree they were instrumented on at the G.A. Pearson Natural Area (GPNA) site and their experimental group. The purpose of these data is to understand how ponderosa pine trees in restored (thinned and burned) vs. unrestored (no treatment) areas are responding to drought and seasonal precipitation. These data use radial growth and soil moisture data to answer the following question: how are active season length, growth on different time scales (weekly, monthly, seasonally, and annually), growth during dry periods and after precipitation events, and environmental and biological drivers of radial growth different between restored versus unrestored areas?

Air temperature↗

DOE OSTI2022

Accounting for uncertainty sources in pyrometric measurements: Variables affecting Surface Temperature measurements using the LLNL Streak Spectrometers

Here we present a detailed analysis of the error sensitivities of the LLNL temperature analysis package used in analyzing streak spectrometer data. These sensitivities include noise, temperature, wavelength, emissivity, missing bands, and missing data points. The analysis package was tested for its ability to accurately reconstruct randomly generated noisy data and found to perform within specifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Relative Contributions of Atmospheric, Oceanic, and Coupled Processes to North Pacific and North Atlantic Variability

Patterns of sea surface temperature (SST) variability over the northern oceans arise from a combination of atmospheric, oceanic, and coupled processes. In this work, we use a novel methodology and a suite of observations to quantify the processes contributing to the dominant patterns of interannual SST variability over these regions. We decompose the upper ocean heat content tendency associated with such dominant patterns into contributions from different heat fluxes: (a) atmospherically driven, (b) surface feedbacks, and (c) oceanic. We find that in the subtropics, cloud radiative flux, turbulent heat flux, and residual oceanic processes each contributes substantially to North Pacific SST variability, whereas turbulent heat flux primarily induces North Atlantic SST variability. Cloud radiative fluxes therefore provide a major source of interannual SST variability in the North Pacific but not in the North Atlantic. In midlatitudes, SST fluctuations over the northern oceans are driven by the combination of turbulent and oceanic heat fluxes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Developing Metrics to Evaluate the Skill and Credibility of Downscaling

In this project, our overarching goal was to evaluate the skill and credibility of statistical and dynamical downscaling techniques for reconstructing the historical climate record and projecting future climate change. This report summarizes our approach and accomplishments. In the first year of the project, the priority was to generate new data sets. For Year 2, the overall goal was to focus comparisons during the historical period and for a subset of variables, especially temperature and precipitation. In Year 3 and beyond, the focus was mainly on comparisons and metrics for future climate change simulations. To quantify the uncertainty associated with gridded dataset choice, the UCLA group compared eight gridded temperature datasets, examining climatologies, variability, and trends. The Scripps group completed statistical downscaling of NARR with the LOCA method, and UCLA and Scripps collaborated on a comparison of historical and end-of-century temperature and precipitation patterns over California from WRF, LOCA-WRF, and LOCA-Livneh. In addition to downscaling temperature and precipitation, the Scripps group has spent considerable time working on near-surface wind and humidity downscaling. The Santa Clara group assembled the Variable Infiltration Capacity (VIC) for nine snow-dominated river basins draining the western slopes of the Sierra Nevada range and assessed the role of snow albedo feedback in their VIC simulations. The results will help inform future downscaling studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Developing Metrics to Evaluate the Skill and Credibility of Downscaling (Final Report)

54 ENVIRONMENTAL SCIENCES↗