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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamical Signatures of Thermotoga maritima Maltose-Binding Proteins Affected by Ligand Binding

Functional segregation among protein isoforms depends on the interplay of their overall structures and the molecular dynamics of these structures. Thermotoga maritima maltose-binding protein (tmMBP) isoforms show size-dependent differential binding of maltose and malto-oligomers while maintaining remarkable fold conservation. This differential behavior needs detailed characterization in native-like aqueous conditions to understand the effects of protein dynamics on ligand binding and recognition. Small-angle neutron scattering (SANS), neutron spin echo (NSE) spectroscopy, and dynamic light scattering (DLS) were used in conjunction with previously published computational molecular dynamics (MD) simulations to understand the dynamic behavior of tmMBPs experimentally. SANS provided information on the overall structure of the molecules, while NSE was used to determine the dynamics in the nanosecond time scale. Both tmMBP2 and tmMBP3 have a bidomain architecture linked with a flexible hinge, with the binding pocket sitting in the cleft between the two domains. tmMBP2 and tmMBP3 showed different solution dynamics, with the translational and rotational components dominating the dynamics of both systems, resulting in a clear differentiation of their diffusion pattern. A faster dynamics component was also observed and was attributed to segmental dynamics. Differences observed between the ligand-free (apo) and ligand-bound (holo) states of the two proteins are attributed to conformational entropy. Our results highlight the intricacies of how structure and dynamics can together shape binding to a repertoire of substrates in structurally similar proteins.

Diffusion↗
DOE OSTI2019

Crystal Chemistry, Band-Gap Red Shift, and Electrocatalytic Activity of Iron-Doped Gallium Oxide Ceramics

This work for the first time unfurls the fundamental mechanisms and set the stage for an approach to derive electrocatalytic activity, which is otherwise not possible, in a traditionally known wide band gap oxide material. Specifically, we report on the tunable optical properties, in terms of wide spectral selectivity and red-shifted band gap, and electrocatalytic behavior of iron (Fe) doped gallium oxide (ß-Ga 2 O 3 ) model system. X-ray diffraction (XRD) studies of sintered Ga 2-x Fe x O 3 (GFO) (0.0 ≤ x ≤ 0.3) compounds provide evidence for the Fe 3+ substitution at Ga 3+ site without any secondary phase formation. Rietveld refinement of XRD patterns reveal that the GFO compounds crystallize in monoclinic crystal symmetry with C2/m space group. The electronic structure of the GFO compounds probed using X-ray photoelectron spectroscopy data reveals that, at lower concentrations of Fe exhibits mixed chemical valence states (Fe 3+ , Fe 2+ ), whereas single chemical valence state (Fe 3+ ) is evident for higher Fe content (x = 0.20-0.30). The optical absorption spectra reveal a significant red shift in the optical band gap with Fe doping. The origin of the significant red shift even at low concentrations of Fe (x = 0.05) is attributed to the strong sp-d exchange interaction originated from the 3 d 5 electrons of Fe 3+ . The optical absorption edge observed at ˜450 nm with lower intensity is the characteristic of Fe doped compounds associated with Fe 3+ - Fe 3+ double excitation process. Coupled with optical band gap red shift, electrocatalytic studies of GFO compounds reveals that, interestingly, Fe doped Ga 2 O 3 compound exhibits electrocatalytic activity in contrast to intrinsic Ga 2 O 3 . Fe doped samples (GFO) demonstrated appreciable electrocatalytic activity towards the generation of H 2 through electrocatalytic water splitting. An onset potential and tafel slope of GFO compounds includes: ~900 mV, ~210 mVdec -1 (x = 0.15) and ~1036 mV, ~290 mVdec-1 (x = 0.30). The electrocatalytic activity of Fe doped Ga-oxide compounds is attributed to cumulative effect of different mechanisms such as; doping resulted new catalytic centers, enhanced conductivity and electron mobility. Hence, in this report, for the first time, we explored a new pathway; electrocatalytic behavior of Fe doped Ga 2 O 3 resulted due to Fe chemical states, red shift in optical band gap. The implications derived from this work may be applicable to a large class of compounds and further options may be available to design functional materials for electrocatalytic energy production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structure–Transport Properties Governing the Interplay in Humidity-Dependent Mixed Ionic and Electronic Conduction of Conjugated Polyelectrolytes

Polymeric mixed ionic-electronic conductors (MIECs) are of broad interest in the field of energy storage and conversion, opto-electronics, and bioelectronics. A class of polymeric MIECs are conjugated polyelectrolytes (CPEs), which possess a p-conjugated backbone imparting electronic transport characteristics along with side chains comprised of a pendant ionic group to allow for ionic transport. Here, our study focuses on the humidity-dependent structure-transport properties of poly[3-(potassium-n-alkanoate) thiophene-2,5-diyl] (P3KnT) CPEs with varied side-chain lengths of n = 4, 5, 6, and 7. UV-Vis spectroscopy along with electronic paramagnetic resonance (EPR) spectroscopy reveal the infiltration of water leads to a hydrated, self-doped state that allows for electronic transport. The resulting humidity-dependent ionic conductivity (σ i ) of the thin films shows a monotonic increase with relative humidity (RH) while electronic conductivity (σ e ) follows a nonmonotonic profile. The values of σ e continue to rise with increasing RH reaching a local maximum after which σ e begins to decrease. P3KnTs with higher n values demonstrate greater resiliency to increasing RH before suffering decrease in σ e . This drop in σ e is attributed to two factors. First, disruption of the locally-ordered π-stacked domains observed through in situ humidity-dependent grazing incidence wide angle X-ray scattering (GIWAXS) experiments can account for some of the decrease in σ e . A second and more dominant factor is attributed to the swelling of the amorphous domains where electronic transport pathways connecting ordered domains are impeded. P3K7T is most resilient to swelling (based on ellipsometry and water uptake measurements) where sufficient hydration allows for high σ i (1.0 × 10 -1 S/cm at 95% RH) while not substantially disrupting σ e (1.7 × 10 -2 S/cm at 85% RH and 8.0 × 10 -3 S/cm at 95% RH). Overall, our study highlights the complexity of balancing electronic and ionic transport in hydrated CPEs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sublimation Kinetics for Individual Graphite and Graphene Nanoparticles (NPs): NP-to-NP Variations and Evolving Structure-Kinetics and Structure-Emissivity Relationships

A single nanoparticle (NP) mass spectrometry method was used to measure sublimation rates as a function of nanoparticle temperature (T NP ) for sets of individual graphite and graphene NPs. Initially, the NP sublimation rates were ~400 times faster than that for bulk graphite, and there were large NP-to-NP variations. Over time, the rates slowed substantially, though remaining well above the bulk rate. The initial activation energies (E a values) were correspondingly low and doubled as a few monolayer’s worth of material were sublimed from the surfaces. The high initial rates and low E a values are attributed to large numbers of edge, defect, and other low coordination sites on the NP surfaces, and the changes are attributed to atomic-scale “smoothing” of the surface by preferential sublimation of the less stable sites. The emissivity of the NPs also changed after heating, most frequently increasing. Here, the emissivity and sublimation rates were anti-correlated, leading to the conclusion that high densities of low-coordination sites on the NP surfaces enhances sublimation but suppresses emissivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Electronic Spin Qubit Candidates Arrayed within Layered Two-Dimensional Polymers

Molecular electronic spin qubits are promising candidates for quantum information science applications because they can be reliably produced and engineered via chemical design. Embedding electronic spin qubits within two-dimensional polymers (2DPs) offers the possibility to systematically engineer inter-qubit interactions while maintaining long coherence times, both of which are prerequisites to their technological utility. Here, we introduce electronic spin qubits into a diamagnetic 2DP by n-doping naphthalene diimide subunits with varying amounts of CoCp 2 and analyze their spin densities by quantitative electronic paramagnetic resonance spectroscopy. Low spin densities (e.g., 6.0 × 10 12 spins mm –3 ) enable lengthy spin–lattice (T 1 ) and spin–spin relaxation (T 2 ) times across a range of temperatures, ranging from T 1 values of 164 ms at 10 K to 30.2 μs at 296 K and T 2 values of 2.36 μs at 10 K to 0.49 μs at 296 K for the lowest spin density sample examined. Higher spin densities and temperatures were both found to diminish T 1 times, which we attribute to detrimental cross-relaxation from spin–spin dipolar interactions and spin–phonon coupling, respectively. Higher spin densities decreased T 2 times and modulated the T 2 temperature dependence. Here, we attribute these differences to the competition between hyperfine and dipolar interactions for electron spin decoherence, with the dominant interaction transitioning from the former to the latter as spin density and temperature increase. Overall, this investigation demonstrates that dispersing electronic spin qubits within layered 2DPs enables chemical control of their inter-qubit interactions and spin decoherence times.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Bottlebrush Block Copolymers at the Interface of Immiscible Liquids: Adsorption and Lateral Packing

Amphiphilic bottlebrush block copolymers (BBCPs), having a hydrophilic bottlebrush polymer (BP) linked covalently to a hydrophobic BP, were found to segregate to liquid-liquid interfaces to minimize the free energy of the system. The key parameter influencing the outcome of the experiments is the ratio between the degree of polymerization of the backbone (NBB) and that of the side-chain brushes (NSC). Specifically, a spherical, star-like configuration results when NBB < NSC, while a cylindrical, bottlebrush-like shape is preferred when NBB > NSC. Dynamic interfacial tension (γ) and fluorescence recovery after photobleaching (FRAP) measurements show that the BBCP configuration influences the areal density and in-plane diffusion at the fluid interface. The characteristic relaxation times associated with BBCP adsorption (τA) and reorganization (τR) were determined by fitting time-dependent interfacial tension measurements to a sum of two exponential relaxation functions. Both τA and τR initially increased with NBB up to 92 repeat units, due to the larger hydrodynamic radius in solution and slower in-plane diffusivity, attributed to a shorter cross-sectional diameter of the side-chains near the block junction. This trend reversed at NBB = 190, with shorter τA and τR attributed to increased segregation strength and exposure of the bare water/toluene interface due to tilting and/or wiggling of the backbone chains, respectively. The adsorption energy barrier decreased with higher NBB, due to a reduced BBCP packing density at the fluid interface. In conclusion, this study provides fundamental insights into macromolecular assembly at fluid interfaces, as it pertains to unique bottlebrush block architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis

The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dynamic Atomistic Polar Structure Underpins Ultrahigh Linear Electro-Optic Coefficient in Transparent Ferroelectric Ceramics

Transparent ferroelectrics with high linear electro-optic (EO) coefficients are critical for advanced electro-optical devices. However, achieving optical transparency in ferroelectric ceramics remains challenging due to visible light scattering caused by defects such as domain walls, grain boundaries, and pores. Here, we report the successful fabrication of transparent ferroelectric ceramics through innovative chemical composition design and an advanced two-step sintering process in the La-doped Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 system. The optical transparency, which is near the theoretical upper limit, can be attributed to the wide band gap and the minimization of light scattering of defects. By minimizing porosity and engineering grain/domain sizes to differ significantly from the wavelengths of visible light, we suppress scattering, achieving optical transparency near the theoretical upper limit. Strikingly, these ceramics exhibit an ultrahigh linear EO coefficient of ∼1417 pm/V, over 65 times greater than that of LiNbO 3 single crystals, the current industry standard. We attribute this exceptional performance to dynamic atomistic polar structures within switchable, thermally stable domains, which enhance electronic polarization sensitivity. This mechanism is corroborated by dielectric spectroscopy, high-resolution transmission electron microscopy and simulation. Our findings offer insights into the design of cost-effective transparent materials with exceptional EO properties, paving the way for next-generation electro-optical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Climatology and Evolution of the Antarctic Peninsula Föhn Wind-Induced Melt Regime From 1979–2018

Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that föhn wind-induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn-induced melt. Long-term föhn-induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short–Term Memory Models for Soil Moisture Predictions

Recently, recurrent deep networks have shown promise to harness newly available satellite–sensed data for long–term soil moisture projections. However, to be useful in forecasting, deep networks must also provide uncertainty estimates. Here we evaluated Monte Carlo dropout with an input–dependent data noise term (MCD+N), an efficient uncertainty estimation framework originally developed in computer vision, for hydrologic time series predictions. MCD+N simultaneously estimates a heteroscedastic input–dependent data noise term (a trained error model attributable to observational noise) and a network weight uncertainty term (attributable to insufficiently constrained model parameters). Although MCD+N has appealing features, many heuristic approximations were employed during its derivation, and rigorous evaluations and evidence of its asserted capability to detect dissimilarity were lacking. To address this, we provided an in–depth evaluation of the scheme's potential and limitations. We showed that for reproducing soil moisture dynamics recorded by the Soil Moisture Active Passive (SMAP) mission, MCD+N indeed gave a good estimate of predictive error, provided that we tuned a hyperparameter and used a representative training data set. The input–dependent term responded strongly to observational noise, while the model term clearly acted as a detector for physiographic dissimilarity from the training data, behaving as intended. However, when the training and test data were characteristically different, the input–dependent term could be misled, undermining its reliability. Additionally, due to the data–driven nature of the model, data noise also influences network weight uncertainty, and therefore the two uncertainty terms are correlated. Altogether, this approach has promise, but care is needed to interpret the results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗
DOE OSTI2022

Rain-Induced Stratification of the Equatorial Indian Ocean and Its Potential Feedback to the Atmosphere

Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine-scale observations of the upper ocean collected during the Dynamics of the Madden-Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well-resolved output from an existing regional atmospheric simulation. RL influence on the ocean-atmosphere system is evaluated through analysis of RL-induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1°C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL-induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave

Abstract Daily maximum temperatures during the 2021 heatwave in the Pacific Northwest United States and Canada shattered century old records. Multiple causal factors, including anthropogenic climate change, contributed to these high temperatures, challenging traditional methods of attributing human influence. We demonstrate that the observed 2021 daily maximum temperatures are far above the bounds of Generalized Extreme Value distributions fitted from historical data. Hence, confidence in Granger causal inference statements about the human influence on this heatwave is low. Alternatively, we present a more conditional hindcast attribution study using two regional models. We performed ensembles of simulations of the heatwave to investigate how the event would have changed if it had occurred without anthropogenic climate change and with future warming. We found that global warming caused a ∼0.8°C–1°C increase in heatwave temperatures. Future warming would lead to a ∼5°C increase in heatwave temperature by the end of the 21st century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying the Value of Technology and Policy Innovation in Water Resource Portfolios

Abstract Recent water infrastructure planning models demonstrate that explicitly accounting for hydrological changes in long‐term water planning reduces costs and increases system robustness. However, current models do not consider the effects of other changes occurring in the system over long time horizons, namely technology and policy innovation. By leveraging state‐of‐the‐art dynamic control techniques, we propose a suite of tools that explicitly relate hydrologic change, technology innovation, and policy interventions to system‐level water costs. We design robust and cost‐optimal strategies for water supply expansion under hydrological variability and hundreds of plausible innovation states, for example, treatment cost and diversification improvements, deployment times, and demand reduction campaigns. Our approach circumvents the need to pre‐assign probabilities to innovation scenarios and, instead, directly calculates the effects of technology, policy, and hydrology variations on system cost and planning strategies to identify avenues of impactful innovation. These tools can support technology development hubs, urban, state, and federal water planners in anticipating cost implications of technology and policy innovation at the local level, and prioritizing high‐impact innovation goals based on quantitative targets. Results for the case study of Santa Barbara, California identify high‐value innovation attributes for the city as well as combinations of innovation attributes across technologies and policies, and quantify their potential utility cost reductions.

13 HYDRO ENERGY↗
DOE OSTI2023

Anthropogenic Aerosols Contribute to the Recent Decline in Precipitation Over the U.S. Southwest

Abstract The winter‐spring precipitation over the Southwestern United States (SWUS) decreased since 1980. It is frequently attributed to Pacific internal decadal variability, but recent studies found anthropogenic aerosols (AA) can also induce a transition to a negative Pacific Decadal Variability (PDV) phase. We revisit the attribution of SWUS drying by quantifying the contributions of anthropogenically forced decadal Pacific Sea Surface Temperatures (SSTs). Applying a low‐frequency component analysis to observations, Community Earth System Model version 2 (CESM2) all‐forcings and single‐forcing large ensembles, we find up to 42% of the observed precipitation trend to be related to the AA‐induced negative PDV‐like pattern, which is driven by the emission shift from the Western to the Eastern Hemisphere. In CESM2, other radiative forcings counteract the influence of AA, but it remains unclear whether the model correctly simulates this balance. This implies that the near‐future trajectories of these forcings, in particular Asian aerosols, are important for projections of SWUS precipitation.

Geology↗
DOE OSTI2023

Carbon Outgassing in the Antarctic Circumpolar Current Is Supported by Ekman Transport From the Sea Ice Zone in an Observation‐Based Seasonal Mixed‐Layer Budget

Despite its importance for the global cycling of carbon, there are still large gaps in our understanding of the processes driving annual and seasonal carbon fluxes in the high-latitude Southern Ocean. This is due in part to a historical paucity of observations in this remote, turbulent, and seasonally ice-covered region. Here, we use autonomous biogeochemical float data spanning 6 full seasonal cycles and with circumpolar coverage of the Southern Ocean, complemented by atmospheric reanalysis, to construct a monthly climatology of the mixed layer budget of dissolved inorganic carbon (DIC). We investigate the processes that determine the annual mean and seasonal cycle of DIC fluxes in two different zones of the Southern Ocean—the Sea Ice Zone (SIZ) and Antarctic Southern Zone (ASZ). We find that, annually, mixing with carbon-rich waters at the base of the mixed layer supplies DIC which is, in the ASZ, either used for net biological production or outgassed to the atmosphere. In contrast, in the SIZ, where carbon outgassing and the biological pump are weaker, the surplus of DIC is instead advected northward to the ASZ. In other words, carbon outgassing in the southern Antarctic Circumpolar Current (ACC), which has been attributed to remineralized carbon from deep water upwelled in the ACC, is also due to the wind-driven transport of DIC from the SIZ. These results stem from the first observation-based carbon budget of the circumpolar Southern Ocean and thus provide a useful benchmark to evaluate climate models, which have significant biases in this region.

Sauvé, Jade↗
DOE OSTI2024

Estimating the CO 2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

Abstract The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO 2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO 2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO 2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m −2 yr −1 ppm −1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO 2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO 2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO 2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO 2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO 2 concentrations.

Environmental Sciences & Ecology↗
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗