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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Candidate strongly lensed type Ia supernovae in the Zwicky Transient Facility archive

Gravitationally lensed type Ia supernovae (glSNe Ia) are unique astronomical tools that can be used to study cosmological parameters, distributions of dark matter, the astrophysics of the supernovae, and the intervening lensing galaxies themselves. A small number of highly magnified glSNe Ia have been discovered by ground-based telescopes such as the Zwicky Transient Facility (ZTF), but simulations predict that a fainter, undetected population may also exist. We present a systematic search for glSNe Ia in the ZTF archive of alerts distributed from June 1 2019 to September 1 2022. Using the AMPEL platform, we developed a pipeline that distinguishes candidate glSNe Ia from other variable sources. Initial cuts were applied to the ZTF alert photometry (with constraints on the peak absolute magnitude and the distance to a catalogue-matched galaxy, as examples) before forced photometry was obtained for the remaining candidates. Additional cuts were applied to refine the candidates based on their light curve colours, lens galaxy colours, and the resulting parameters from fits to the SALT2 SN Ia template. The candidates were also cross-matched with the DESI spectroscopic catalogue. Seven transients were identified that passed all the cuts and had an associated galaxy DESI redshift, which we present as glSN Ia candidates. Although superluminous supernovae (SLSNe) cannot be fully rejected as contaminants, two events, ZTF19abpjicm and ZTF22aahmovu, are significantly different from typical SLSNe and their light curves can be modelled as two-image glSN Ia systems. From this two-image modelling, we estimate time delays of 22 ± 3 and 34 ± 1 days for the two events, respectively, which suggests that we have uncovered a population of glSNe Ia with longer time delays. The pipeline is efficient and sensitive enough to parse full alert streams. It is currently being applied to the live ZTF alert stream to identify and follow-up future candidates while active. This pipeline could be the foundation for glSNe Ia searches in future surveys, such as the Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Single‐Step Deformation Processing of Ultrathin Lithium Foil and Strip

Abstract Next‐generation, high‐efficiency energy storage and conversion systems require development of lithium metal batteries. But the high cost of production and constraints on thickness of lithium (anode) foils continue to limit adoption for integration into battery cell architectures. Here, a novel lithium anode manufacturing solution is demonstrated – single‐step production of ultrathin gauge foil formats directly from solid ingot. Hybrid cutting‐based deformation processes, involving large plastic strains and strain rates, produce foil to sub‐10 µm thickness, with surface quality even superior to present Li anode processing routes. Energy analysis shows the single‐stage processing is ≈50% more efficient than conventional processing by extrusion‐rolling. Through in situ force measurements and high‐speed imaging of the cutting it also characterize – for the first time – the flow stress of Li to strain rates of 800 sec −1 , revealing a power‐law relationship. The results present a paradigm shift in manufacturing and integration of solid lithium anodes for energy applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Machining of Thin-Walled Structures From Stiffness-Driven Additively Manufactured Preform Geometry

Additive manufacturing provides the means to build component preforms with reduced excess material to create functional parts. In the case of aero-structural and aero-engine components, additive manufacturing technologies offer the possibility to substantially reduce the volume of material to be removed by machining operations. To achieve this objective, the preform must be built with the minimum material necessary to contain the final geometry and simultaneously provide enough stiffness to withstand the magnitude of the machining forces. This work describes a computationally efficient method to calculate the geometry required from the preform to reliably manufacture typical thin-walled structures via finish machining processes. This is achieved by defining the preform with near constant static stiffness across the width of the preform, in combination with a prescribed magnitude of stiffness at the top edge of the preform. The prescribed static stiffness is the function of the machining force magnitude, a direct consequence of the preselected cutting conditions. In conclusion, this article illustrates the application of the method to a straight single boundary thin-walled structure as an introduction case and for ease of description.

Additive manufacturing↗
DOE OSTI2025

Postdoctoral insights on mentoring excellence: a framework for best practices at Sandia National labs

The Sandia National Laboratories Strategic Plan FY24-FY27, updated for FY25, outlines Sandia’s two Big Labs-wide Goals, Accelerate Innovation and Lead in Modern Engineering. The goal of Accelerate Innovation is that “by FY27, Sandia will be a leader in scientific, engineering and operational innovation and an employer of choice for highly innovative and creative talent.” Sandia’s postdocs are leaders in innovation, well-versed in emerging techniques and cutting-edge methods, and capable of acting as a highly agile technical force across domains at the lab. As a federally funded research and development center (FFRDC), Sandia National Laboratories attracts top doctoral talent by offering a unique opportunity for postdoctoral researchers to develop at the crossroads of government, academia, and industry, working in multi-disciplinary teams and performing cutting-edge, mission-specific research that responds to immediate needs of national interest. However, this creates unique opportunities and demands of both postdoctoral appointees and the Sandia staff who act as their mentors, making mentorship key to attract talent. Since 2007 the Sandia Postdoctoral Development (SPD) Board, originally Postdoc To Professional (PD2P), a networking group at Sandia composed of a voluntary board of current postdocs and two staff liaisons, has advocated for postdoctoral development within Sandia National Labs. In this white paper, SPD board members and the Sandia Postdoctoral Development Office, organized in 2019, have come together to develop a comprehensive overview of the postdoctoral mentoring landscape at Sandia National Labs as we currently know it. By scouring various forms of data from efforts since 2018, we’ve compiled a community-derived perspective on what makes postdoctoral mentorship at Sandia unique. First, we analyze working sessions held between mentors and mentees to develop a comprehensive map of who is involved in postdoctoral mentorship at the lab and how the responsibilities are divided amongst mentors and mentees. We then combine multiple forms of data, including exit surveys, annual surveys, and community workshops, to identify the specific challenges that mentors and mentees encounter at the national lab. Finally, we use text mining and sentiment analysis to analyze mentoring award data to develop an idea of what postdocs are self-identifying as excellent mentorship within the lab. It is our goal that this white paper act as an ongoing resource to the postdoc and postdoc mentoring communities and provide a firm foundation for further conversations on the future of postdoctoral mentorship at Sandia National Labs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Recent advances in the mechanical characterization of shales at nano-to micro-scales: A review

Understanding geomechanical properties of shales, such as stiffness properties and fracture toughness, is important in different areas of petroleum industry-related activities. Nanoscale to microscale experiments on shale samples require a much smaller sample compared to macroscale experiments. This is a major advantage because irregularly-shaped drill cuttings from shales can be used for experimental analyses, such as nanoindentation or atomic force microscopy (AFM). However, characterization of mechanical properties at the nano- to micro-scale is a relatively new addition to classical geomechanical experiments on shales, and there is still a lack of both fundamental knowledge and standard procedures for conducting experiments at this scale. The theoretical principles of nanoindentation-based (Gridded Nanoindentation and Modulus Mapping) and AFM-based experimental methods (PeakForce QNMTM and AFM-IR), a review of the literature results and the major findings are explained in Sections 2 and 3, respectively. These experimental techniques are compared in Section 4. Overall, nanoindentation experiments reveal the anisotropy of organic matter (OM)-rich shales at the nanoscale, and it is shown that gridded nanoindentation is capable of characterizing different minerals with respect to their stiffness properties. The capability of AFM (PeakForce QNMTM) to differentiate between different minerals within OM-rich shales with distinct mechanical properties, in particular stiffness, is also demonstrated. It is shown that AFM-based methods are more straightforward than gridded-nanoindentation to characterize OM-rich shales based on the stiffness of different constituent minerals of OM-rich shales, but nanoindentation can apply higher loads than AFM-based methods. Therefore, they can measure creep properties and fracture toughness of OM-rich shales. Here, it is shown that the OM is the most compliant part of the OM-rich shales, but it is still unclear if thermal maturation has an impact on the stiffness of OM. It is necessary to investigate if there is a relationship between the OM type (kerogen, bitumen, etc.) and mechanical properties such as Young’s modulus and Creep rate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗
DOE OSTI2022

A Strong-QCD Regime Measurement of the Proton’s Spin Structure

The theory of the strong force, Quantum Chromodynamics (QCD) remains one of the most important ways to understand the fundamental properties of ordinary matter. However, at low momentum transfer Q 2 , in the regime where the strong force becomes extremely strong, our understanding of QCD for ordinary nucleons becomes hazy. Several cutting edge theories such as Chiral Perturbation Theory (χPT) and Lattice QCD have provided valuable predictions in this regime, but Lattice QCD has not yet extended predictions of many important quantities to this kinematic region, and Chiral Perturbation Theory has faced several important disagreements with experimental data for the neutron over the last several decades. It is therefore of extreme importance to have a benchmark of experimental data in the low energy regime for the proton’s behavior, as a test of leading theories for the behavior of QCD in this regime. The E08-027 (g2p) experiment ran at Jefferson Lab in 2012 with the goal of collecting this valuable data, and though I was still completing my undergraduate studies at the time, I became involved in the analysis in 2015 and built on the previous work to complete it and analyze the exciting results. This experiment achieved a high precision measurement of the spin structure functions g1 and g2 for the proton, quantities which describe the internal spin structure of the proton. These measurements were taken in the valuable low Q 2 region described above, and used to extract several moments of these spin structure functions which can be directly compared to the cutting-edge predictions of Chiral Perturbation Theory. Though the experiment’s timeline was such that I didn’t have a chance to work directly on the experimental setup, I had the opportunity to acquire hands-on experience working on a polarized target at UNH which is very similar to the crucial polarized target used in the g2p experiment. Full details of the g2p experiment and my experimental work at the University of New Hampshire are presented in this thesis, as well as a detailed description of the analysis process and the exciting benchmark results, which serve as a direct test of all current and future theories of QCD in the low-Q 2 regime.

Ruth, David↗
DOE OSTI2020

Quantifying resolution in cosmological N- body simulations using self-similarity

ABSTRACT We demonstrate that testing for self-similarity in scale-free simulations provides an excellent tool to quantify the resolution at small scales of cosmological N-body simulations. Analysing two-point correlation functions measured in simulations using abacus , we show how observed deviations from self-similarity reveal the range of time and distance scales in which convergence is obtained. While the well-converged scales show accuracy below 1 per cent, our results show that, with a small force softening length, the spatial resolution is essentially determined by the mass resolution. At later times, the lower cut-off scale on convergence evolves in comoving units as a−1/2 (a being the scale factor), consistent with a hypothesis that it is set by two-body collisionality. A corollary of our results is that N-body simulations, particularly at high red-shift, contain a significant spatial range in which clustering appears converged with respect to the time-stepping and force softening but has not actually converged to the physical continuum result. The method developed can be applied to determine the resolution of any clustering statistic and extended to infer resolution limits for non-scale-free simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2020

Ultrasonic in-situ water-cut measurement using ultrasonic oil-water separation for affecting sound speed calibration

An apparatus and method for the separation of an oil-water mixture into its components are described. An acoustic radiation force moves oil droplets to the nodes of an acoustic standing wave generated in a vertical column containing the oil-water mixture. Once the droplets are sufficiently close together, attractive forces become dominant and the droplets may coalesce to form larger droplets, which have greater buoyancy, and separation of the mixture into a layer of oil and a layer of water occurs, not possible by simple gravitational separation. Acoustically-driven oil-water separation may be used for water-cut measurements in oil production wells, since separation of the oil from the water permits accurate sound speed measurements to be made for both the oil and the water, thereby allowing frequent in situ calibrations of the apparatus to determine whether sound speed measurements on the mixture are accurate in the event that one or both of the mixture constituents is changing.

02 PETROLEUM↗
DOE OSTI2024

3D‐Printed Carbon Nanoneedle Electrodes for Dopamine Detection in Drosophila

In vivo electrochemistry in small brain regions or synapses requires nanoelectrodes with long straight tips for submicron scale measurements. Nanoelectrodes can be fabricated using a Nanoscribe two-photon printer, but annealed tips curl if they are long and thin. We propose a new pulling-force strategy to fabricate a straight carbon nanoneedle structure. A micron-width bridge is printed between two blocks. The annealed structure shrinks during pyrolysis, and the blocks create a pulling force to form a long, thin, and straight carbon bridge. Parameterization study and COMSOL modeling indicate changes in the block size, bridge size and length affect the pulling force and bridge shrinkage. Electrodes were printed on niobium wires, insulated with aluminum oxide, and the bridge cut with focused ion beam (FIB) to expose the nanoneedle tip. Annealed needle diameters ranged from 400 nm to 5.25 μm and length varied from 50.5 μm to 146 μm. The electrochemical properties are similar to glassy carbon, with good performance for dopamine detection with fast-scan cyclic voltammetry. Nanoelectrodes enable biological applications, such as dopamine detection in a specific Drosophila brain region. Long and thin nanoneedles are generally useful for other applications such as cellular sensing, drug delivery, or gas sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The homogenous alternative to biomineralization: Zn- and Mn-rich materials enable sharp organismal “tools” that reduce force requirements

We measured hardness, modulus of elasticity, and, for the first time, loss tangent, energy of fracture, abrasion resistance, and impact resistance of zinc- and manganese-enriched materials from fangs, stings and other “tools” of an ant, spider, scorpion and nereid worm. The mechanical properties of the Zn- and Mn-materials tended to cluster together between plain and biomineralized “tool” materials, with the hardness reaching, and most abrasion resistance values exceeding, those of calcified salmon teeth and crab claws. Atom probe tomography indicated that Zn was distributed homogeneously on a nanometer scale and likely bound as individual atoms to more than ¼ of the protein residues in ant mandibular teeth. This homogeneity appears to enable sharper, more precisely sculpted “tools” than materials with biomineral inclusions do, and also eliminates interfaces with the inclusions that could be susceptible to fracture. Based on contact mechanics and simplified models, we hypothesize that, relative to plain materials, the higher elastic modulus, hardness and abrasion resistance minimize temporary or permanent tool blunting, resulting in a roughly 2/3 reduction in the force, energy, and muscle mass required to initiate puncture of stiff materials, and even greater force reductions when the cumulative effects of abrasion are considered. We suggest that the sharpness-related force reductions lead to significant energy savings, and can also enable organisms, especially smaller ones, to puncture, cut, and grasp objects that would not be accessible with plain or biomineralized “tools”.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations

We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem. Our main focus is on compilation with trapped-ion simulators using Pauli-X operations and all-to-all Ising Hamiltonian HIsing evolution generated by Molmer-Sorensen or optical dipole force interactions, though some of our results also apply to standard gate-based compilations. Our results are based on principles of graph sparsification and decomposition; the former reduces the number of edges in a graph while maintaining its cut structure, while the latter breaks a weighted graph into a small number of unweighted graphs. Though these techniques have been used as heuristics in various hybrid quantum algorithms, there have been no guarantees on their performance, to the best of our knowledge. This work provides the first provable guarantees using sparsification and decomposition to improve quantum noise resilience and reduce quantum circuit complexity. For quantum hardware that uses edge-by-edge QAOA compilations, sparsification leads to a direct reduction in circuit complexity. For trapped-ion quantum simulators implementing all-to-all HIsing pulses, we show that for a (1−ϵ) factor loss in the Max-Cut approximation (ϵ>0), our compilations improve the (worst-case) number of HIsing pulses from O(n2) to O(nlog(n/ϵ)) and the (worst-case) number of Pauli-X bit flips from O(n2) to O(nlog(n/ϵ)ϵ2) for n-node graphs. This is an asymptotic improvement for any constant ϵ>0. We demonstrate that significant improvements to the approximation ratio are obtained using decomposition in simulated trapped-ion experiments with dephasing noise. We further present a generic argument showing that sparsification results in an exponentially improved circuit fidelity lower bound in digital computing schemes based on one- and two-qubit gates, which are relevant to a wide variety of hardwares such as superconducting qubits and certain neutral atom or trapped ion setups, and more sophisticated noise models. We anticipate these approximate compilation techniques will be useful tools in a variety of future quantum computing experiments.

Moondra, Jai [Georgia Institute of Technology]↗
DOE OSTI2021

The effect of longer-range waviness on X-ray reflectivity measurements

A model for calculating the X-ray reflectivity (XRR) of surfaces to extract both roughness and waviness features is presented. Expressions of reflectivity intensity are derived as a function of root-mean-square (RMS) roughness σ, RMS waviness σ L , and the cut-off frequency between the features ω 0 . Experiments were conducted at the Advanced Light Source at Lawrence Berkeley National Laboratory, beamline 8.3.2, on BK7 glass manufactured with a multi-step polishing process to validate the model, and were compared with atomic force microscopy (AFM), Fizeau interferometry and surface profilometry measurements. Here, the parameter results and their deviations for XRR measurements were σ = 2.9 ± 0.2 nm and σ L = 14.6 ± 0.5 nm with a wavelength cut-off of 1/(18 ± 2) µm –1 , while the results from the AFM, Fizeau and profilometry measurements were σ AFM = 3.4 ± 0.4 nm, σ L,Fizeau = 21.6 nm, σ prof = 4.0 ± 0.1 nm, and σ L,prof = 21.4 ± 0.1 nm with cut-offs for the profilometry and Fizeau measurements limited to frequencies of (1/16) µm –1 to (1/4) mm –1 .

42 ENGINEERING↗
DOE OSTI2024

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Strategizing emission cuts in China to mitigate short-term warming from clean air policies

Aerosol-cloud interactions remain one of the largest sources of uncertainty in estimates of anthropogenic climate forcing. Reducing aerosols to achieve air quality improvement and climate goals can cause unintended warming due to the weakening of aerosol cooling. Here we investigate the climate impacts of spatially optimized sulfur dioxide (SO 2 ) emission reduction under a carbon-neutral pathway in China. We find that targeting reduction in highly polluted regions in China significantly suppresses the rise in effective radiative forcing due to aerosol-cloud interactions (ERF aci ) during 2020–2060. Owing to the nonlinear aerosol-cloud interaction, the optimized emission reduction strategy limits the regional average increase in ERF aci of more than 0.89 W m −2 to less than 0.13 W m −2 in the short-term future during 2020–2040 and weakens the ERF aci increase by two-thirds in 2060 over China. These findings demonstrate the critical role of targeted emission control in mitigating short-term climate risks while pursuing air quality goals.

Zhu, Yitong [Nanjing Univ. of Information Science ↗
DOE OSTI2024

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Environmental Sciences & Ecology↗
DOE OSTI2021

Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Abstract. Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

54 ENVIRONMENTAL SCIENCES↗