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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Visualizing Size-Dependent Dynamics of CeO 2-δ {100}-Supported CoO x Nanoparticles Under CO 2 Hydrogenation Conditions

Carbon dioxide is a major greenhouse gas. In order to optimize processes focused on its chemical valorization, one needs detailed information about the effects of CO 2 and/or CO 2 /H 2 mixtures on the structure and morphology of metal/oxide catalysts. Here, in this study, the evolution of a catalyst with cobalt supported on CeO 2 -cube nanostructures under CO 2 hydrogenation conditions was investigated by using a set of in situ characterization techniques (X-ray absorption fine structure, X-ray diffraction, diffuse reflectance infrared Fourier transform spectroscopy, and environmental transmission electron microscopy (TEM)). The {100} facets of the ceria support displayed an unexpectedly high stability due to strong interactions with the aggregates of cobalt oxide. A significant influence of interfacial bonding between CoO x and CeO 2-δ {100} is evident through a clear preference in the orientation of CoO x nanoparticles (NPs) with respect to the substrate. For initially reduced Co/CeO 2 -cube nanostructures, a kinetically controlled oxidation of cobalt upon the introduction of CO 2 was observed during the early stages of CO 2 hydrogenation. Environmental TEM revealed the size-dependent morphological behavior of cobalt oxide NPs due to strong interactions with the CeO 2 {100} surface. When the environment was switched from H 2 to a mixture of H 2 and CO 2 at 250 °C, small CoO x NPs (in the largest dimension < 2.5 nm) rapidly transformed from a pyramidal three-dimensional (3D) form to a planar, monatomic layer attached to the concurrently oxidized CeO 2-δ {100} surface. This maximizes the number of sites available for the binding of CO 2 or reaction intermediates. The shape transformation reflected the oxophilic character of cobalt and strong metal–support interactions. The removal of CO 2 from the gas phase led to a reduction of the cobalt oxide NPs by hydrogen and a reversible two-dimensional → 3D transformation. In contrast, no significant morphological changes, apart from further oxidation, were observed for big CoO x NPs (in the largest dimension > 3 nm). These trends are not seen for nanoparticles of noble metals. The observed morphological and structural changes in the small CoO x NPs affected the stability of reaction intermediates and modified the selectivity of the CoO x /CeO 2 catalyst system for methane production.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Shake-Down Spectroscopy as State- and Site-Specific Probe of Ultrafast Chemical Dynamics

Tracking the multifarious ultrafast electronic and structural changes occurring in a molecule during a photochemical transformation is a challenging endeavor that benefits from recent experimental and computational progress in time-resolved techniques. Measurements of valence electronic states, which provide a global picture of the bonding structure of the molecule, and core electronic states, which provide insight into the local environment, traditionally require different approaches and are often studied separately. Here, we demonstrate that X-ray pulses from a seeded free-electron laser (FEL) enable the measurement of high-resolution, time-resolved X-ray photoelectron spectra (XPS) that capture weak satellite states resulting from shake-down processes in a valence-excited molecule. This approach effectively combines the advantages of both valence- and core-state investigations. We applied this method to investigate photoexcited CS2 molecules, where the role of internal conversion (IC) and intersystem crossing (ISC) in determining the predissociation dynamics is controversial. We present XPS spectra from photoexcited CS2, obtained at the FERMI FEL. High-resolution measurements, compared to the corresponding spectra obtained from accurate multireference quantum chemical calculations, reveal that shake-down satellite channels are highly sensitive to both valence electronic and geometric changes. Previous studies of the predissociation dynamics have led to uncertain assignments of the branching between singlet and triplet excited states. We derive a propensity rule that demonstrates the spin-selectivity of the shake-downs. This selectivity allows us to unequivocally assign contributions from the bright and dark singlet excited states, with populations tracked along the predissociation dynamic pathway.

Thompson, Henry J. [Univ. of Southampton (United K↗
DOE OSTI2025

Photoredox Catalysis with Spin Magnetic Field Effects: A Scheme for Chiral Resolution

Quantities that break both mirror symmetry and time-reversal symmetry, such as the orbital angular momentum, are known to connect molecular chirality with an applied magnetic field. This concept has led to observations such as magneto-chiral dichroism and chirality-induced spin selectivity (CISS). However, being small, these effects often require additional amplification procedures such as flow chemistry to achieve bulk enantioseparation. In this work, we demonstrate how the magnetic field effect on photogenerated radical pairs, which also breaks time-reversal symmetry, can be harnessed for enantiopurification. Fundamental to this process is the collective decay of the singlet and triplet radical-pair states made possible by an applied magnetic field. Because opposite enantiomers exhibit spin-orbit coupling matrix elements of opposite signs, the singlet and triplet decay channels interfere constructively in one enantiomer. Meanwhile, molecules of the other enantiomer are funnelled into the first enantiomer through excited-state chirality inversion, achieving (dynamic kinetic) chiral resolution. Using an axially chiral binaphthyl derivative and a borane photosensitizer as prototype, we predict an appreciable enantiomeric excess (e.e.) of 90% to be possible at steady state, attained within hundreds of milliseconds when irradiated by a laser. Importantly, our analytical results showcase regimes of perfect enantioselectivity (100% e.e.), accessible by further chemical optimization of the photosensitizer for which general strategies are discussed. Altogether, this work illustrates a so-far untapped but powerful control knob for photoredox catalysis based on spin chemistry principles.

Magnetic properties↗
DOE OSTI2026

Surface-Functionalized, Two-Dimensional Polymer Electrochromic Layers as Ultrafast, Multi-State Infrared Optical Gates

Electrochromic devices have widespread application potential, but the currently available switching speeds limit broad real-world implementation of this technology. Here, we report surface-engineered two-dimensional polymers with ionophilic pores that offer unprecedented switching speeds in solid-state, two-terminal, electrochromic devices. In particular, we demonstrate that a crystalline donor–acceptor 2DP functionalized with ethylene glycol oligomers exhibits multistate infrared absorption that is 4× faster (t c = 320 ms) with 3× coloration efficiency (491 cm 2 C –1 ) compared to an alkyl functionalized 2DP constructed from the same chromophores. The functionalized nanoporous surfaces enable rapid switching in these materials under either oxidative or reductive conditions, allowing us to access a range of robust, stable optical responses in a single electrochromic layer. These attributes led us to leverage surface-functionalized 2DPs as multistate infrared logic gates. Collectively, this work demonstrates that surface engineering of nanoporous crystalline lattices is a promising approach to co-optimize the electronic and ionic conductivities required to achieve rapidly switchable electrochromic layers. Beyond speed and efficiency, the demonstration of multistate infrared characteristics shows that electrochromic frameworks are useful in integrated optoelectronic circuits. This positions surface-engineered 2DPs as improved electrochromic coatings and a new material platform for photonic information processing and adaptive devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Native Architecture of Wheat Straw Cell Walls: A Unified Model from X-ray Scattering and Solid-State NMR

Plant secondary cell walls constitute the dominant reservoir of renewable biomass, comprising tightly packed cellulose, hemicellulose, and lignin at the nanoscale. Recent advances in solid-state NMR spectroscopy and the availability of small-angle X-ray scattering for biomass characterization have led to an accumulation of experimental data on cell wall organization, yet no explicit structure model has simultaneously satisfied both Xray and NMR observations. Using wheat straw as a model system, we propose a structural framework consistent with current knowledge of cellulose biosynthesis, X-ray scattering data, and one- and two-dimensional 13 C solid-state NMR spectra. In this model, 18-chain elementary fibrils align in parallel and populate the cross-section at random. Arabinose-substituted xylan shows no conformational dependence for cellulose-binding in wheat, and only a minor fraction of 2-fold xylan appears in close proximity to cellulose, unlike in Arabidopsis, where xylan is more tightly attached to the cellulose surface. While NMR data cannot unambiguously resolve the internal arrangement of the 18 glucan chains, X-ray scattering profiles uniquely constrain the fibril size and exclude the possibility of tight bundling in the intact walls. The specific interaction between the matrix polymers and the cellulose elementary fibrils must be reconsidered in light of the small interfibril spaces, which bring the matrix components into spatial proximity with cellulose even in the absence of attractive interactions. These findings provide fundamental molecular-level insight into cellulose fibril architecture and matrix−polymer interactions, resolving longstanding discrepancies between spectroscopic and scattering data and advancing our understanding of biopolymer assembly into structurally and functionally versatile lignocellulosic biomaterials.

Carbohydrates↗
DOE OSTI2026

Emissive Colloidal GaAs Quantum Dots

Colloidal quantum dots offer tunable optical properties for optoelectronic applications, but the synthesis of high-quality III–V quantum dots (QDs) other than indium pnictides, and particularly GaAs, has remained elusive due to synthetic challenges. Colloidal GaAs QDs have been reported very recently and have shown only weak band-edge photoluminescence. Here, we demonstrate a large-scale synthesis of colloidal GaAs QDs in molten salts, followed by a high-temperature surface treatment with K 2 S, which removes native oxide and enables uniform zinc chalcogenide shell growth on GaAs. These QDs demonstrate bright band-edge photoluminescence and electroluminescence in QD LED devices. Low-temperature spectroscopy reveals a well-resolved exciton fine structure with distinct bright-state splitting and temperature-independent decay dynamics from 4 to 100 K. These results establish a practical pathway for the preparation of high-quality colloidal GaAs QDs from molten salts with potential applications in quantum technologies and optoelectronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Deactivating Emission in Azulene via Solvent-Induced (Anti)Aromaticity

Anti-Kasha emission is a coveted feature in optoelectronics, with promise in areas such as imaging, sensing, and the production of white-light LEDs via dual-photon emission. Despite being a rare feature in organic systems, anti-Kasha emission is readily observed in the deceptively simple molecule azulene, which possesses two bright singlet states in the UV–visible spectrum and readily emits from the S 2 state. With dominant anti-Kasha emission and decades of synthetic study, azulene is a perfect candidate for novel material fabrication; however, large gaps persist in understanding the photophysics of even the simple parent compound. These range from competing, experimentally unverified models of azulene reactivity and aromaticity to the unexplained deactivation of anti-Kasha emission in a host of azulene derivatives. Herein, we use fluorescence and transient absorption spectroscopies to explore the detailed solvent dependence of azulene photophysics. We discover a tunable reduction in the S 2 lifetime via weak complexation with aromatic solvents. Interestingly, our results are independent of polarity, highlighting the primacy of peripherally delocalized 10-π Hückel aromaticity over zwitterionic character. When the dipolar character is enhanced through chemical functionalization, we observe even greater sensitivity to solvent aromaticity and more rapid quenching, revealing the role of conical intersections in azulenes with zwitterionic excited states. Overall, this work provides essential mechanistic insight into the photophysics of azulene and reveals a simple new approach to control the excited-state aromaticity and anti-Kasha emission in this class of materials.

Absorption↗
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Early Career Perspectives to Broaden the Scope of Critical Zone Science

Abstract Critical Zone (CZ) scientists have advanced understanding of Earth's surface through process‐based research that quantifies water, energy, and mass fluxes in predominantly undisturbed systems. However, the CZ is being increasingly altered by humans through climate and land use change. Expanding the scope of CZ science to include both human‐ and non‐human controls on the CZ is important for understanding anthropogenic impacts to Earth's surface processes and ecosystem services. Here, we share perspectives from predominantly U.S.‐based, early career CZ scientists centered around broadening the scope of CZ science to focus on societally relevant science through a transdisciplinary science framework. We call for increased training on transdisciplinary methods and collaboration opportunities across disciplines and with stakeholders to foster a scientific community that values transdisciplinary science alongside physical science. Here, we build on existing transdisciplinary research frameworks by highlighting the need for institutional support to include and educate graduate students throughout the research processes. We also call for graduate‐student‐led initiatives to increase their own exposure to transdisciplinary science through activities such as transdisciplinary‐focused seminars and symposiums, volunteering with local conservation groups, and participating in internships outside academia.

Warix, Sara [University of Utah Department of Geol↗
DOE OSTI2025

Permafrost, Peatland, and Cropland Regions Are Key to Reconciling North American Carbon Sink Estimates

Persistent discrepancies between bottom-up, terrestrial biosphere models (TBMs), and top-down, atmospheric inversions, have made it difficult to quantify the magnitude of the North American terrestrial carbon sink. Previous studies have compared aggregated continent-scale estimates of carbon fluxes from TBMs and inversions for all of North America, but this provides limited insights into finer-scale mismatches that contribute to the overall discrepancies. Here we evaluate agreement between TBM and inversion carbon flux estimates at 1° × 1° resolution to provide more direct insights into where models disagree and what underlying factors drive discrepancies. We find that the additional carbon uptake estimated by inversions, in just 16% of the area of North America, is large enough to account for the discrepancy between TBMs and inversions across the whole continent. The majority of these differences occur in permafrost, peatland, and cropland regions. In these regions, we find a higher likelihood of potential biases in the weaker sink estimates from TBMs, suggesting that the stronger sink implied by inversions is more likely to be realistic. However, the current observational coverage is insufficient for fully assessing the causes of discrepancies or the magnitude of biases in either approach. Encouragingly, improved representation of agricultural processes in a TBM led to better agreement with inversions in croplands. Efforts to accurately model cropland dynamics will help improve agreement between TBMs and inversions. Overall, this work presents a clear path for reconciling the discrepancies between inversion and TBM estimates of the North American carbon sink that have persisted for two decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

On the Importance of Precipitation‐Induced Surface Sensible Heat Flux for Diurnal Cycle of Precipitation in the Maritime Continent

Abstract The Maritime Continent (MC) exhibits a pronounced diurnal cycle in precipitation, with many high‐resolution models overestimating the diurnal peak and predicting earlier precipitation over the islands than observed. We hypothesize that part of this model bias comes from ignoring precipitation‐induced surface sensible heat flux ( Q P ). To test this conjecture, we performed simulations with and without Q P for April 2009 and June 2006. The inclusion of Q P reduced the bias in diurnal peak precipitation amplitude by 83% in April 2009 and 23% in June 2006. Similarly, the bias in precipitation peak timing decreased by 26% and 15%, respectively. This bias reduction was even more prominent during periods of heavier rainfall. This improvement in both the amplitude and phase of diurnal precipitation also led to a reduction in bias for total precipitation by ∼10%. These findings suggest that Q P cannot be neglected over the MC, particularly during heavy precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Drivers of Atlantic Tropical Cyclogenesis: African Easterly Waves and the Environment

Abstract Tropical cyclone (TC) genesis requires favorable environmental conditions and an initial disturbance, which, in the North Atlantic, is often an African easterly wave (AEW). Although studies have examined how AEWs and the environment affect TC genesis, their relative importance is less understood. Here, we examine whether AEW strength or sea surface temperatures (SSTs) are the primary drivers of TC genesis. Regional model simulations were performed to address how increased AEW strength and SSTs affect the frequency of AEWs that develop into TCs in a year with below‐average TCs with AEW origins and SSTs. We found that environmental favorability plays a larger role than AEW strength in driving the frequency of TCs with AEW origins. Strengthening the AEWs did not affect the frequency of AEWs that develop into TCs due to low environmental favorability. Warmer SSTs led to increased environmental favorability and a statistically significant increase in TCs with AEW origins.

Bercos‐Hickey, Emily [Climate and Ecosystem Scienc↗
DOE OSTI2024

Derivations of the Total Radiation Belt Electron Content

Abstract We present multiple derivations of the Total Radiation Belt Electron Content (TRBEC), an indicator of the global number of electrons that instantaneously occupy the radiation belts. Derived from electron flux measurements, the TRBEC reduces the spatial information into a scalar quantity that concisely describes global aspects of the system. This index provides a simple, global, and long‐term assessment of the radiation belts that enables systematic analysis. In this work, we examine the TRBEC using the adiabatic invariants of which has been used in previous articles as this coordinate system removes reversible adiabatic effects. We then introduce a new expression to compute the TRBEC using the non‐adiabatic coordinates of , relevant in the contexts of energetic electron precipitation, chorus, and hiss scattering where adiabatic invariant quantities are no longer conserved. From both expressions of the TRBEC we demonstrate that an erroneous factor of that appeared in previous works using the adiabatic derivation led to an overestimate of the reported electron populations. In addition, we quantify electron loss in the outer radiation belt via a case study using the Van Allen Probes data over a 20‐day period from March 2013 specifying particle populations both in terms of the aforementioned adiabatic and non‐adiabatic variables. The total number of electrons in the outer radiation belt reached upwards of electrons at the peak of the storm, a rest mass of roughly 10 g.

Astronomy & Astrophysics↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming

Abstract Understanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO 2 effluxes during the initial years of soil warming. The bias against observed CO 2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models. Plain Language Summary A major uncertainty in projecting how much soil organic carbon (SOC) will be converted to CO 2 as a consequence of climate change is related to how soil microbes may adapt to increasing soil temperatures. While this “microbial thermal adaptation” has been shown to occur in short‐term lab incubation experiments, its effect on SOC cycling on a decadal timescale is not clear. To address this knowledge gap, a mechanistic SOC model was used to simulate data collected from a 13‐year soil warming experiment, to assess how microbial thermal adaptation affects predicted SOC losses upon soil warming. The model results show that incorporating microbial thermal adaptation into the model led to reduced CO 2 effluxes from the soil to the atmosphere compared to the common approach of omitting this mechanism. Our results imply that projected SOC losses for the decades to come may be reduced when this mechanism is incorporated in land models. We therefore advocate for more research on the mechanisms controlling microbial thermal adaptation, and how to implement this mechanism in SOC models. Key Points A crucial aspect of soil organic carbon (SOC) models is the representation of soil microbes Predicted soil CO 2 fluxes upon soil warming are reduced when accounting for microbial thermal adaptation On a centennial time scale, this thermal adaptation results in up to a factor of three lower predicted SOC loss

Van de Broek, Marijn↗
DOE OSTI2025

Representing Soil Microbial Dynamics and Organo‐Mineral Interactions in the E3SM Land Model (ELM‐ReSOM)

Explicit representation of soil microbial processes and interactions with biotic and abiotic processes in Earth System Models (ESMs) remains limited, despite their importance in biogeochemical cycles. To address this gap, which hinders prediction of global biogeochemial cycling and responses to atmospheric conditions, we integrated a microbe- and mineral-surface-explicit model, the Reaction-network-based model of soil organic matter and Microbes (ReSOM), into the Energy Exascale ESM (E3SM) land model (ELM). Here, we describe ELM-ReSOM and show a case study at a conifer forest in California. ELM-ReSOM accurately simulated surface CO 2 fluxes and SOM stocks, demonstrating improved representations of microbial and mineral interactions compared to the default ELM. We examined ELM-ReSOM sensitivity to microbial traits, enzyme properties, and organo-mineral interactions. Microbial traits such as the maximum mortality rate, transporter-density scaling factor, and maximum monomer assimilation rate were strong controllers of heterotrophic respiration, while these microbial traits and enzyme-related properties collectively influenced SOM stocks. Mineral surfaces primarily affected SOM stocks by adsorbing enzymes, thereby limiting depolymerization. Synergies among processes led to stronger impacts of parameters when evaluated together versus separately (i.e., most parameters had greater indirect than direct effects). For example, due to interactions of microbial necromass with mineral surface adsorption, the indirect effect of the maximum microbial mortality rate was 33% larger than its direct effect on SOM stock. Thus, microbial and enzyme dynamics and their interactions with mineral surfaces play critical roles in SOM cycling. Tackling the challenges of microbe-explicit models will advance understanding and modeling of SOM dynamics.

Tao, Jing [Lawrence Berkeley National Laboratory (↗
DOE OSTI2026

Evaluation of top-down and bottom-up global terrestrial respiration estimates and their mismatch with model simulations

Terrestrial respiration is one of the most poorly understood processes in the global carbon cycle, making respiration predictions uncertain. However, expanding observations and machine learning approaches have led to a proliferation of estimates. We compiled total ecosystem and heterotrophic respiration estimates derived from top-down atmospheric inversions and bottom-up upscaling of ecosystem observations and compared them with dynamic vegetation models (DGVM) simulations over the 1980-2020 period. Our analysis revealed a convergence in mean annual global total ecosystem respiration estimates between top-down 97.1 (± SD 6.8) PgC yr-1 and bottom-up 98.5 (+/-13.4) PgC yr-1, which were both significantly lower than the ensemble mean from DGVMs estimates 133.7 (±4.7) PgC yr-1. We also found similar temporal trends between top-down estimates with a mean of 0.075 (±0.05) PgC yr-2, and bottom-up estimates of 0.05 (±0.05) PgC yr-2, which were 5 to 7 times smaller than the ensemble mean trend of 0.34 PgC yr-2 simulated by DGVMs. Global heterotrophic respiration showed much less agreement, ranging from top-down estimates of 42.7 (±4.0) PgC yr-1 to bottom-up estimates of 51.5 (±4.0) PgC yr-1 and a significantly larger ensemble model mean estimate of 60.8 PgC yr-1 (±1.9). The temporal trends in observation-based bottom-up estimates of heterotrophic respiration of 0.03 PgC yr-2 were five times lower than the model ensemble mean 0.15 PgC yr-2. Large regional disagreements in heterotrophic respiration estimates and simulations were evident in tropical and boreal latitudes. Therefore, improved regional and heterotrophic respiration estimates are necessary to reduce uncertainties regarding the future vulnerability of soil carbon.

Ballantyne, Ashley↗