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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stable and radioactive carbon isotope partitioning in soils and saturated systems: a reactive transport modeling benchmark study

This benchmark provides the first rigorous test of a three-isotope system [ 12 C, 13 C, and 14 C] subject to the combined effects of radioactive decay and both stable equilibrium and kinetic fractionation. We present a series of problems building in complexity based on the cycling of carbon in both organic and inorganic forms. The key components implement (1) equilibrium fractionation between multiple coexisting carbon species as a function of pH, (2) radioactive decay of radiocarbon with associated mass-dependent speciation demonstrating appropriate correction of the Δ 14 C value in agreement with reporting convention, and (3) kinetic stable isotope fractionation due to the oxidation of organic carbon to inorganic forms as a function of time and space in an open, through-flowing system. Participating RTM codes are CrunchTope, ToughReact, Hytec, and The Geochemist’s Workbench. Across all problem levels, simulation results from all RTMs demonstrate good agreement.

58 GEOSCIENCES↗
DOE OSTI2026

Data for Zheng et al. (2025), "AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry"

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties, but current models inadequately represent these complexities. This dataset contains the observational and modeling data from Zheng et al. (2025), which described a process-based modeling framework that couples soil solution chemistry with microbial carbon cycling reactions to study the impacts of soil salinization. This conceptual model is implemented numerically into the open-source geochemical program PHREEQC 3.0 (Parkhurst and Appelo, 2013). This dataset consists of: - Figure2_AquaMEND_salinity_buffer: Contains model simulation outputs to assess the impact of three different cation exchange and surface complexation processes on salinity buffering (Fig. 2 from Zheng et al. 2025). - Figure3_Salinity_function: Contains salinity function fitting for literature data (Fig. 3 from Zheng et al. 2025). - Figure4_AquaMEND_microbial_mechanisms: Contains model simulation outputs for testing various microbial process-based hypotheses related to soil salinization, including microbial mortality, carbon use efficiency (CUE), extracellular enzyme activity, and other microbial mechanisms (Fig. 4 from Zheng et al. 2025). - Figure5_AquaMEND_Redox: Contains on model simulation outputs to evaluate shifts among key redox processes, such as aerobic respiration, sulfate reduction, and methanogenesis (Fig.5 from Zheng et al. 2025). - Figure6_AquaMEND_sorption: Contains on model simulation outputs for investigating the effects of salinity on dissolved organic matter (DOM) sorption and desorption processes (Fig. 6 from Zheng et al. 2025). - Figure7_AquaMEND_process_couple: Contains on model simulation outputs for exploring coupled biotic-abiotic processes and their interactions (Fig. 7 from Zheng et al. 2025). - data: Includes datasets used to develop salinity response functions and evaluate salinity buffering capacity. Datasets for MEND model calibration. - database: Contains the `.dat` file required by PHREEQC for model execution. - README.md: A Markdown plain text file describing the computational tools and directories. Files are a mixture of plain text CSV (comma-separated value) and plain text *.dat files written by the model; no special software is required to read them.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL SALINIT↗
DOE OSTI2022

Carbon acquisition ecological strategies to connect soil microbial biodiversity and carbon cycling

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. Further, we describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Pronounced and unavoidable impacts of low-end global warming on northern high-latitude land ecosystems

Arctic ecosystems are particularly vulnerable to climate change because of Arctic amplification. Here, we assessed the climatic impacts of low-end, 1.5 °C, and 2.0 °C global temperature increases above pre-industrial levels, on the warming of terrestrial ecosystems in northern high latitudes (NHL, above 60 °N including pan-Arctic tundra and boreal forests) under the framework of the Inter-Sectoral Impact Model Intercomparison Project phase 2b protocol. We analyzed the simulated changes of net primary productivity, vegetation biomass, and soil carbon stocks of eight ecosystem models that were forced by the projections of four global climate models and two atmospheric greenhouse gas pathways (RCP2.6 and RCP6.0). Our results showed that considerable impacts on ecosystem carbon budgets, particularly primary productivity and vegetation biomass, are very likely to occur in the NHL areas. The models agreed on increases in primary productivity and biomass accumulation, despite considerable inter-model and inter-scenario differences in the magnitudes of the responses. The inter-model variability highlighted the inadequacies of the present models, which fail to consider important components such as permafrost and wildfire. The simulated impacts were attributable primarily to the rapid temperature increases in the NHL and the greater sensitivity of northern vegetation to warming, which contrasted with the less pronounced responses of soil carbon stocks. The simulated increases of vegetation biomass by 30–60 Pg C in this century have implications for climate policy such as the Paris Agreement. Comparison between the results at two warming levels showed the effectiveness of emission reductions in ameliorating the impacts and revealed unavoidable impacts for which adaptation options are urgently needed in the NHL ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying Variability and Controls of Riverine Dissolved Organic Carbon Exported to Arctic Coastal Margins of North America (Final Report)

This project involved implementation and application of a coupled permafrost hydrology and dissolved organic carbon process models to investigate how spatial and seasonal variations in terrestrial hydrology and soil freeze/thaw dynamics influence the mobilization, loading, and export of organic carbon to the stream network for selected arctic basins across northern Alaska and northwest Canada. The model simulations were constrained by detailed observations of in- stream chemistry, soil active layer profile moisture and temperature dynamics, streamflow, soil carbon inventories and satellite microwave remote sensing based assessments of surface soil freeze-thaw dynamics. We developed and applied the numerical modeling and data analysis, incorporating observed data for calibration and validation, in order to investigate the terrestrial hydrology, permafrost dynamics, and associated DOC production and loading to rivers across a region encompassing watersheds draining to the coast. The project produced six publications and three datasets archived in public repositories.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

A permafrost implementation in the simple carbon-climate model Hector v.2.3.pf

Permafrost currently stores more than a fourth of global soil carbon. A warming climate makes this carbon increasingly vulnerable to decomposition and release into the atmosphere in the form of greenhouse gases. The resulting climate feedback can be estimated using land surface models, but the high complexity and computational cost of these models make it challenging to use them for estimating uncertainty, exploring novel scenarios, and coupling with other models. We have added a representation of permafrost to the simple, open-source global carbon–climate model Hector, calibrated to be consistent with both historical data and 21st century Earth system model projections of permafrost thaw. We include permafrost as a separate land carbon pool that becomes available for decomposition into both methane (CH4) and carbon dioxide (CO2) once thawed; the thaw rate is controlled by region-specific air temperature increases from a preindustrial baseline. We found that by 2100 thawed permafrost carbon emissions increased Hector’s atmospheric CO2 concentration by 5 %–7 % and the atmospheric CH4 concentration by 7 %–12 %, depending on the future scenario, resulting in 0.2–0.25 ∘C of additional warming over the 21st century. The fraction of thawed permafrost carbon available for decomposition was the most significant parameter controlling the end-of-century temperature change in the model, explaining around 70 % of the temperature variance, and was distantly followed by the initial stock of permafrost carbon, which contributed to about 10 % of the temperature variance. The addition of permafrost in Hector provides a basis for the exploration of a suite of science questions, as Hector can be cheaply run over a wide range of parameter values to explore uncertainty and can be easily coupled with integrated assessment and other human system models to explore the economic consequences of warming from this feedback.

Dawn L. Woodard↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling Interactive Effects of Manganese Bioavailability, Nitrogen Deposition, and Warming on Soil Carbon Storage

Manganese (Mn) is a redox–active micronutrient that has been shown to accelerate plant litter decomposition; however, the effect of Mn–promoted decomposition on soil C storage is unclear. We present a novel biogeochemical model simulating how Mn bioavailability influences soil organic C (SOC) stocks in a soil profile (<50 cm) within a temperate forest. In our model, foliar Mn increased in response to increasing soluble Mn released through Mn–oxide (birnessite) dissolution in mineral soil layers. The ensuing Mn enrichment in leaf litter redistributed Mn to the surface forest floor layer, promoted enzymatic oxidation of lignin, and decreased SOC stocks. Total SOC loss was partially mitigated by accumulation of lignin–oxidation products as mineral–associated organic C. We also explored how Mn–driven changes to C storage interacted with effects of N deposition and warming. Nitrogen enrichment inhibited Mn–dependent lignin degradation, increasing SOC stocks and weakening their dependence on Mn bioavailability. Warming stimulated decomposition and reduced C stocks but was less effective at low Mn bioavailability. Our model results suggest that SOC stocks are sensitive to Mn bioavailability because increased plant uptake redistributes Mn to surface soils where it can enhance litter decomposition. Finally, based on our simulations, we predict that Mn becomes limiting to litter decomposition where Mn is poorly soluble. Depletion of bioavailable Mn or other cofactors that are critical to decomposition could limit the response of organic C stocks to warming over time, but quantitative projections of the role of Mn bioavailability in regulating decomposition requires additional measurements to constrain model uncertainties.

58 GEOSCIENCES↗
NASA NTRS1997

Regional Application of an Ecosystem Production Model for Studies of Biogeochemistry in the...

The degree to which primary production, soil carbon, and trace gas fluxes in tropical forests of the Amazon are limited by moisture availability and other environmental factors was examined using an ecosystem modeling application for the country of Brazil. A regional geographic information system (GIS) serves as the data source of climate drivers, satellite images, land cover, and soil properties for input to the NASA Ames-CASA (Carnegie-Ames-Stanford Approach) model over a 8-km grid resolution. Simulation results supports the hypothesis that net primary production (NPP) is limited by cloud interception of solar radiation over the humid northwestern portion of the region. Peak annual rates for NPP of nearly 1.4 kg C m-2yr -1are localized in the seasonally dry eastern Amazon in areas that we assume are primarily deep-rooted evergreen forest cover. Regional effects of forest conversion on NPP and soil carbon content are indicated in the model results, especially in seasonally dry areas. Comparison of model flux predictions along selected eco-climatic transects reveal moisture, soil, and land use controls on gradients of ecosystem production and soil trace gas emissions (CO2, N2O, and NO). These results are used to formulate a series of research hypotheses for testing in the next phase of regional modeling, which includes recalibration of the light-use efficiency term in CASA using field measurements of NPP, and refinements of vegetation index and soil property (texture and potential rooting depth) maps for the region.

Potter, C. S.↗
DOE OSTI2023

Microscale spatiotemporal patterns of water, soil organic carbon, and enzymes in plant litter detritusphere

Understanding biophysical and biochemical processes in detritusphere is critical to quantifying and modeling plant residue decomposition dynamics and subsequent soil organic carbon (C) accrual. The objectives of the study were to explore (i) micro-environmental conditions within the detritusphere formed around decomposing corn and soybean leaves, and (ii) the relationships between moisture distribution, enzyme activity, and C dynamics during decomposition. We assessed spatial and temporal dynamics of moisture distribution using X-ray and neutron computed tomography, and activities of β-glucosidase and chitinase, two enzymes involved in soil C and N processing, using zymography. We used 13 C labeled residue to track residue contribution to atmospheric CO 2 and soil organic C. Moisture redistribution pattern varied depending on the residue type. While the water was immediately absorbed by the corn leaves and maintained afterward, switchgrass leaves absorbed water more slowly and created water-deficient zones within ~1mm from the residue. This initial moisture depletion led to lower chitinase activity and residue-derived CO 2 emissions in switchgrass. In contrast to chitinase, β-glucosidase activity was influenced by a combination of vegetation history and residue type, and it was higher when the origin of the residue matched the vegetation history of the soil. Pore size had an opposite impact on the studied enzymes, supporting the notion that contrasting soil pore architecture can stimulate activities of different enzymes through a selection of dominant enzyme producers. We concluded that the decomposition dynamics of plant residues is not only a simple function of residue chemistry, but rather a combined effect of the vegetation history, in part through its effect on microbial community composition, the plant residue chemical and likely physical characteristics, and the soil pore structure in the detritusphere. Together, they create temporally dynamic micro-environmental conditions influencing decomposition. Specifically, our study demonstrated that the initial micro-environment formulated in detritusphere can play an important role in enzyme activities and consequent C dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗
NASA NTRS2022

Modeling Demographic-Driven Vegetation Dynamics and Ecosystem Biogeochemical Cycling in NASA GISS’s Earth System Model (ModelE-BiomeE v.1.0)

We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.

Biogeochemical cycles↗
DOE OSTI2022

Plant litter traits control microbial decomposition and drive soil carbon stabilization

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13 C enriched aboveground and belowground litters to soils and traced the fate of the 13 C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modelling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modelling SOC transformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A permafrost implementation in the simple carbon–climate model Hector v.2.3pf

Abstract. Permafrost currently stores more than a fourth of global soil carbon. A warming climate makes this carbon increasingly vulnerable to decomposition and release into the atmosphere in the form of greenhouse gases. The resulting climate feedback can be estimated using land surface models, but the high complexity and computational cost of these models make it challenging to use them for estimating uncertainty, exploring novel scenarios, and coupling with other models. We have added a representation of permafrost to the simple, open-source global carbon–climate model Hector, calibrated to be consistent with both historical data and 21st century Earth system model projections of permafrost thaw. We include permafrost as a separate land carbon pool that becomes available for decomposition into both methane (CH4) and carbon dioxide (CO2) once thawed; the thaw rate is controlled by region-specific air temperature increases from a preindustrial baseline. We found that by 2100 thawed permafrost carbon emissions increased Hector’s atmospheric CO2 concentration by 5 %–7 % and the atmospheric CH4 concentration by 7 %–12 %, depending on the future scenario, resulting in 0.2–0.25 ∘C of additional warming over the 21st century. The fraction of thawed permafrost carbon available for decomposition was the most significant parameter controlling the end-of-century temperature change in the model, explaining around 70 % of the temperature variance, and was distantly followed by the initial stock of permafrost carbon, which contributed to about 10 % of the temperature variance. The addition of permafrost in Hector provides a basis for the exploration of a suite of science questions, as Hector can be cheaply run over a wide range of parameter values to explore uncertainty and can be easily coupled with integrated assessment and other human system models to explore the economic consequences of warming from this feedback.

54 ENVIRONMENTAL SCIENCES↗