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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site

Here, this study presents a long‐term three‐dimensional large‐scale forcing data set (VARANAL3D) derived from the three‐dimensional constrained variational analysis (3DCVA) method at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site from 2004 to 2018. Building on the same input data sets as the conventional continuous forcing data set (VARANAL), VARANAL3D maintains overall consistency in domain‐averaged fields while introducing spatial variability, offering critical insights into the influence of mesoscale synoptic systems on cloud‐related processes. Evaluations are conducted across four cloud and precipitation regimes: Clear‐sky, Shallow‐clouds, Afternoon‐precipitation, and Nocturnal‐precipitation, presenting high consistency of the domain‐mean forcing data sets while emphasizing the role of subdomain forcing variability particularly in precipitating regimes. Single column model (SCM) simulations demonstrate that subdomain VARANAL3D forcing improves cloud and precipitation representation, with the ensemble outperforming domain‐mean forcing in three cloudy and precipitating regimes. Overall, these results highlight VARANAL3D's value for investigating the impacts of spatial variability of large‐scale forcing on atmospheric processes. The VARANAL3D data set provides new opportunities for evaluating model physics, advancing the development of scale‐aware parameterizations and deepening our understanding of cloud and precipitation dynamics.

Environmental sciences↗
DOE OSTI2024

Soil microenvironmental variation drives below‐ground trait variation and interacts with macroclimate to structure above‐ground trait variation of arctic shrubs

Abstract Intraspecific trait variation can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. However, our understanding of its patterns and environmental drivers across different spatial scales is incomplete, especially in understudied regions like the Arctic. To fill this knowledge gap, we examined above‐ground and below‐ground traits from three shrub taxa expanding across the tundra biome and evaluated their relationships with multiple microenvironmental and macroclimatic factors. The traits reflected plant size and structure (plant height, leaf area and root to shoot ratio), leaf economics (specific leaf area, nitrogen content), and root economics and collaboration with mycorrhizal fungi (specific root length, root tissue density, nitrogen content, and ectomycorrhizal colonisation intensity). We also measured leaf and root δ 15 N and leaf δ 13 C to characterise nitrogen source and acquisition pathways and plant water stress. Traits were measured in replicated plots ( N = 135) varying in soil microclimate, thaw depth and organic layer thickness established across five sites spanning a macroclimate gradient in northern Alaska. This hierarchical design allowed us to disentangle the independent and combined effects of fine‐scale and broad‐scale factors on intraspecific trait variation. We found substantial intraspecific variation at fine spatial scales for most traits and less variation along the macroclimate gradient and between shrub taxa. Consistent with these patterns, microenvironmental factors, mainly soil moisture and thaw depth, interacted with macroclimate, mainly climatic water deficit, to structure size‐structural and leaf trait variation. In contrast, most root traits responded additively to thaw depth and macroclimate. Synthesis . Our results demonstrate that above‐ground and below‐ground tundra shrub traits respond differently to microenvironmental and macroclimatic variation. These differing responses contribute to substantial trait variation at fine spatial scales and may decouple above‐ground and below‐ground trait responses to climate change.

Environmental Sciences & Ecology↗
DOE OSTI2026

Data for Genetic Variation in Zea mays Influences Microbial Nitrification and DeNitrification in Conventional Agroecosystems

Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds ( Z. mays ssp. mays ), ex-PVP hybrids ( Z. mays ssp. may s), and teosinte ( Z. mays ssp. mexicana and Z. mays ssp. parviglumis ). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Nitrogen↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2022

Impact of Measured Spectrum Variation on Solar Photovoltaic Efficiencies Worldwide

In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.

energy yield↗
DOE OSTI2025

Multi-site Milling Strategy Reveals Significant Variation in Biomass Composition of Switchgrass ( Panicum virgatum ) Grown at Ten Locations

Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.

09 BIOMASS FUELS↗
DOE OSTI2023

What controls the interannual variation of Hadley cell extent in the Northern Hemisphere: physical mechanism and empirical model for edge variation

Abstract The Hadley circulation is the most prominent atmospheric meridional circulation, reducing the radiatively driven equator-to-pole temperature gradient. While the Hadley cell extent varies by several degrees from year to year, the detailed dynamical mechanisms behind such variations have not been well elucidated. During the expanded phase of the Hadley cell, many regions on the periphery of the subtropics experience unfavorable climatic conditions. In this study, using ERA5 reanalysis data, we examine the physical chain of events responsible for the interannual variation of the Hadley cell edge (HCE) latitude in the Northern Hemisphere. This variation is mainly caused by changing eddy activity and wave breaking from both stationary and transient waves. In particular, we show that transient waves cause the HCE to shift poleward by increasing the eddy momentum flux divergence (EMFD) and reducing the baroclinicity over 20°–40°N, shifting the region of peak baroclinicity poleward. El Niño/La Niña and the Arctic Oscillation (AO) account for a significant portion (60%) of the interannual fluctuation of the HCE latitude. Through the poleward displacement of eddy activity, La Niña and a positive AO state are associated with the poleward shift of the HCE. The analysis of 28 CMIP5 models reveals statistical relationships between EMFD, vertical shear, and HCE latitude similar to those observed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2024

Induced and natural variation affect traits independently in hybrid Populus

Abstract The genetic control of many plant traits can be highly complex. Both allelic variation (sequence change) and dosage variation (copy number change) contribute to a plant's phenotype. While numerous studies have investigated the effect of allelic or dosage variation, very few have documented both within the same system, leaving their relative contribution to phenotypic effects unclear. The Populus genome is highly polymorphic, and poplars are fairly tolerant of gene dosage variation. Here, using a previously established Populus hybrid F1 population, we assessed and compared the effect of natural allelic variation and induced dosage variation on biomass, phenology, and leaf morphology traits. We identified QTLs for many of these traits, but our results indicate limited overlap between the QTLs associated with natural allelic variation and induced dosage variation. Additionally, the integration of data from both allelic and dosage variation identifies a larger set of QTLs that together explain a larger percentage of the phenotypic variance. Finally, our results suggest that the effect of the large indels might mask that of allelic QTLs. Our study helps clarify the relationship between allelic and dosage variation and their effects on quantitative traits.

Guo, Weier (ORCID:0000000251789334)↗
DOE OSTI2024

Daytime variation in the aerosol indirect effect for warm marine boundary layer clouds in the eastern North Atlantic

Abstract. Warm boundary layer clouds in the eastern North Atlantic region exhibit significant diurnal variations in cloud properties. However, the diurnal cycle of the aerosol indirect effect (AIE) for these clouds remains poorly understood. This study takes advantage of recent advancements in the spatial resolution of geostationary satellites to explore the daytime variation in the AIE by estimating the cloud susceptibilities to changes in cloud droplet number concentration (Nd). Cloud retrievals for the month of July over 4 years (2018–2021) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat-11 over this region are analyzed. Our results reveal a significant “U-shaped” daytime cycle in susceptibilities of the cloud liquid water path (LWP), cloud albedo, and cloud fraction. Clouds are found to be more susceptible to Nd perturbations at noon and less susceptible in the morning and evening. The magnitude and sign of cloud susceptibilities depend heavily on the cloud state defined by cloud LWP and precipitation conditions. Non-precipitating thin clouds account for 44 % of all warm boundary layer clouds in July, and they contribute the most to the observed daytime variation. Non-precipitating thick clouds are the least frequent cloud state (10 %), and they exhibit more negative LWP and albedo susceptibilities compared to thin clouds. Precipitating clouds are the dominant cloud state (46 %), but their cloud susceptibilities show minimal variation throughout the day. We find evidence that the daytime variation in LWP and albedo susceptibilities for non-precipitating clouds is influenced by a combination of the diurnal transition between non-precipitating thick and thin clouds and the “lagged” cloud responses to Nd perturbations. The daytime variation in cloud fraction susceptibility for non-precipitating thick clouds can be attributed to the daytime variation in cloud morphology (e.g., overcast or broken). The dissipation and development of clouds do not adequately explain the observed variation in cloud susceptibilities. Additionally, daytime variation in cloud susceptibility is primarily driven by variation in the intensity of cloud response rather than the frequency of occurrence of cloud states. Our results imply that polar-orbiting satellites with an overpass time at 13:30 LT underestimate daytime mean values of cloud susceptibility, as they observe susceptibility daily minima in the study region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning

Reliability issues stemming from device level nonidealities of nonvolatile emerging technologies like ferroelectric field-effect transistors (FeFETs), especially at scaled dimensions, cause substantial degradation in the accuracy of in-memory crossbar-based AI systems. Here, in this work, we present a variation-aware design technique to characterize the device level variations and to mitigate their impact on hardware accuracy employing a Bayesian neural network (BNN) approach. An effective conductance variation model is derived from the experimental measurements of cycle-to-cycle (C2C) and device-to-device (D2D) variations performed on FeFET devices fabricated using 28 nm high-k metal gate technology. The variations were found to be a function of different conductance states within the given programming range, which sharply contrasts earlier efforts where a fixed variation dispersion was considered for all conductance values. Such variation characteristics formulated for three different device sizes at different read voltages were provided as prior variation information to the BNN to yield a more exact and reliable inference. Near-ideal accuracy for shallow networks (MLP5 and LeNet models) on the MNIST dataset and limited accuracy decline by ~3.8%–16.1% for deeper AlexNet models on CIFAR10 dataset under a wide range of variations corresponding to different device sizes and read voltages, demonstrates the efficacy of our proposed device-algorithm co-design technique.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Rubidium and potassium isotopic variations in chondrites and Mars: Accretion signatures and planetary overprints

As moderately volatile elements, isotopes of Rb and K can trace volatilization processes in planetary bodies. Rubidium isotopic data are however very scarce, especially for non-carbonaceous meteorites. Here, in this study, we report combined Rb and K isotopic data (δ 87/85 Rb and δ 41/39 Κ) for 7 ordinary, 6 enstatite, and 4 Martian meteorite falls to understand the causes for the variations in volatile abundances and isotopic compositions. Bulk Rb and K isotopic compositions of planetary bodies are estimated to be (Table 1): Mars +0.10 ± 0.03 ‰ for Rb and -0.26 ± 0.05 ‰ for K, bulk OCs $-0.12^{+0.15}_{-0.24}$ ‰ for Rb and $-0.72^{+0.28}_{-0.41}$ ‰ for K, bulk ECs $-0.02^{+0.29}_{-0.26}$ ‰ for Rb and $-0.33^{+0.37}_{-0.23}$ ‰ for K. The bulk K isotopic compositions of subgroup OCs are estimated to be $-0.72^{+0.26}_{-0.55}$ ‰ for H chondrites, $-0.71^{+0.23}_{-0.39}$ ‰ for L chondrites, and $-0.77^{+0.63}_{-0.30}$ ‰ for LL chondrites. A broad correlation between the Rb and K isotopic compositions of planetary bodies is observed. The correlation follows a slope that is consistent with kinetic evaporation and condensation processes, suggesting volatility-controlled mass-dependent isotope fractionation (as opposed to nucleosynthetic anomalies). Individual ordinary and enstatite chondrites show large Rb and K isotopic variations (-1.02 to +0.29 ‰ for Rb and -0.91 to -0.15 ‰ for K). Samples of lower metamorphic grades display correlated elemental and isotopic fractionations between Rb and K, while samples of higher metamorphic grades show great scatter, suggesting that chondrite parent-body processes have decoupled the two elements and their isotopes at the sample scale. Several processes could have contributed to the observed isotopic variations of Rb and K, including (i) chondrule “nugget effect”, (ii) volatilization during parent-body thermal metamorphism (heat-induced vaporization and gas transport within parent bodies), (iii) thermal diffusion during parent-body metamorphism, and (iv) impact/shock heating. Quantitative modeling of the first two processes suggests that neither of them could produce isotopic variations large enough to explain the observed isotopic variations. Volatilization during parent-body thermal metamorphism [the scenario (ii)], which has been commonly invoked to explain the isotopic variations of volatile elements, is gas transport-limited and its effect on isotopic fractionations of moderately volatile elements should be negligible. Modeling of diffusion processes suggests that (iii) could produce K isotopic variation comparable to the observed variation. The large isotopic variations in non-carbonaceous meteorites are thus most likely due to diffusive redistribution of K and Rb during metamorphism and/or shock-induced heating and vaporization.

58 GEOSCIENCES↗
DOE OSTI2023

Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure

Abstract The distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle. This study provides a high‐resolution portrait of genomic variation in Chinook salmon ( Oncorhynchus tshawytscha ) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered data set of ~13 million single nucleotide polymorphisms (SNPs) from low‐coverage whole genome resequencing of 53 populations (3566 barcoded individuals), we contrasted patterns of genomic structure within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Neutral variation provided support for fine‐scale structure of populations, while allele frequency variation in GREB1L/ROCK1 was highly correlated with mean return timing for early and late migrating populations within each of the lineages ( r 2 = .58–.95; p < .001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream‐type) compared to the other two major lineages, which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.

Horn, Rebekah L.↗
DOE OSTI2026

Variation in supercurrent connectivity and vortex pinning of state-of-the-art pulsed laser deposited RE Ba 2 Cu 3 O 7- x coated conductors

REBa 2 Cu 3 O 7-x (REBCO, RE: rare earth) coated conductors (CCs) suffer from great critical current I c differences between different manufacturers, I c variations within individual manufacturers, and often significant lengthwise fluctuations. The understanding of such variations is complicated by the lack of a direct correlation between I c and the critical current density J c . In fact, although J c is the fundamental property determined by the local vortex pinning landscape, I c is often limited by variable current blocking mechanisms. An important practical complexity is commercial practices of performing J c and I c evaluations based on I c at 77 K and self-field (sf), where connectivity variation dominates over vortex pinning variations. However, at higher fields and lower temperatures, vortex pinning becomes more complex and highly variable, making predictions of I c and J c at arbitrary temperature T, magnetic field H, and field orientation θ, quite uncertain. To address some aspects of this problem, we conducted detailed spool-to-spool performance characterization on recently manufactured REBCO CCs. Despite J c (77 K, sf) varies by only ∼11%, J c (77 K, 1 T) of its minimum and maximum (for H//ab-plane) show variations of ∼21% and ∼32%, respectively. This emphasizes the shortcoming in using J c (77 K, sf) as parameter for evaluating even the low field performance. An even more remarkable spool-to-spool J c variation of ∼68% was observed at 20 K and 15 T for H//ab-plane. To identify the origin of such lack of reproducibility we performed microstructural characterizations, which revealed, within the REBCO layer, large variation in the density of copper oxide (CuO x ) particles ranging from 0.1 to 2 μm in size. We believe that they play a decisive role in reducing the effective cross-section of the REBCO layer by not simply blocking current themselves, but also by nucleating off-axis REBCO grains, whose misoriented grain boundaries adversely impact REBCO grain-to-grain connectivity. The REBCO growth associated with high density CuO x particles also leads to the more disordered spatial arrangement of BaHfO 3 precipitate arrays, which, when self-aligning along the ab-planes, generate stronger pinning enhancing J c (H//ab) at all temperatures. In this way, we established that variations of both connectivity and vortex pinning are thus directly coupled. Our results also explain why the so-called ‘lift-factor’, typically defined by the ratio between I c (T, H) and I c (77 K, sf), frequently turns out to be unreliable.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Incorporating Diurnal and Meter-Scale Variations of Ambient CO 2 Concentrations in Development of Direct Air Capture Technologies

To be implemented on climate-relevant scales, direct air capture of CO 2 (DAC) will require large capital-intensive facilities and careful attention to cost minimization. In making decisions among potential sites for DAC facilities, all of the factors that will impact process cost and efficiency should be considered. In this paper we focus on a factor that has previously received little attention in the DAC community, namely variations in atmospheric conditions on hourly time scales and length scales of meters. We present data curated from extensive previous studies of biosphere-atmosphere fluxes with observations of CO 2 concentration, temperature, and relative humidity (RH) with hourly resolution from many sites in North America. These include locations where typical diurnal variations in CO 2 concentration during summer months exceeds 150 ppm. These variations are larger than the seasonal variations that exist between averaged CO 2 concentrations in winter and summer, and they are highly correlated with diurnal variations in temperature and RH. Diurnal variations are dependent on the height above ground at which CO 2 concentrations are measured, with smaller variations existing at heights of 10 m or more than at ground level. We illustrate the potential implications of these short-term variations for the operation and optimization of a DAC process with process-level calculations for a specific adsorption-based process using amine-rich adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗