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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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NASA NTRS2019

Twentieth Century Redistribution in Climatic Drivers of Global Tree Growth

Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930–1960 CE responded positively to temperature (20.8 ± 3.7 Mio km2; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km2; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant (P < 0.001) decrease in temperature response at cold-dry sites between 1930–1960 and 1960–1990 CE, and the total temperature-limited area shrunk by −8.7 ± 0.6 Mio km2. Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.

Babst, Flurin↗
DOE OSTI2019

Land carbon models underestimate the severity and duration of drought’s impact on plant productivity

The ability to precisely predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0–6 months) between climate anomalies and modeled NPP response. Even though models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO 2 .

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Land carbon models underestimate the severity and duration of drought’s impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0–6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

carbon cycle↗
NASA NTRS2022

Simultaneous Retrieval of Selected Optical Water Quality Indicators From Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (a cdom (440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and a cdom (440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and a cdom (440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and a cdom (440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.

Machine learning↗
DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface Salinity Under Transitioning Ice Cover in the Canada Basin: Climate Model Biases Linked to Vertical Distribution of Fresh Water

Abstract The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper‐ocean over the past three decades. State‐of‐the‐art ice‐ocean models, by contrast, tend to simulate a surface layer that is saltier and less stratified than observed. Here, we examine decadal changes to seasonal processes that may contribute to this wide‐reaching model bias using climate model simulations from the Community Earth System Model and below‐ice observations from the Arctic Ice Dynamics Joint Experiment in 1975 and Ice Tethered Profilers in 2006–2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be mainly attributed to unrealistically deep vertical mixing in the model, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement with broad implications for Arctic sea ice and ecosystem dynamics.

58 GEOSCIENCES↗
NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗
NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗
DOE OSTI2023

Optimizing Hydrogen Fueling Infrastructure Plans on Freight Corridors for Heavy-Duty Fuel Cell Electric Vehicles

The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen-based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This article introduces the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy-duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional-specific vehicle fuel economy and weight, vehicle origin and destination (O-D), and vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal public access locations for hydrogen refueling (constrained by existing fueling stations), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. The framework discussed in this article can be expanded and applied to a larger interstate system, expanded regional corridor, or other transportation network. This article is the third in a series of papers that defined the model development to optimize a national hydrogen refueling infrastructure ecosystem for HD commercial vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Semi‐Mechanistic Model for Partitioning Evapotranspiration Reveals Transpiration Dominates the Water Flux in Drylands

Abstract Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well‐suited to dryland ecosystems, such as high sensitivity of plant water‐use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine‐scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi‐mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process‐based models, and loosely constrains time‐varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross‐wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub‐yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem‐scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.

Environmental Sciences & Ecology↗
DOE OSTI2025

Evaluating post-fire watershed response to varying burn severity and precipitation regimes using fully-distributed and integrated hydrologic models

Wildfires can cause significant changes in vegetation and soil, which may lead to increased surface runoff and soil erosion, thereby affecting water cycling within ecosystems. This study uses the Advanced Terrestrial Simulator (ATS), an integrated and fully distributed hydrologic model at the watershed scale, to examine post-fire hydrologic responses in selected watersheds with varying burn severities in the Pacific Northwest region of the United States. The model integrates surface overland flow, subsurface flow, and canopy biophysical processes. We have developed a new fire module in ATS to account for changes in soil hydraulic properties caused by fire in the topsoil layer. Modeling results indicate that, in the year following a high-severity burn, watershed-averaged evapotranspiration decreases by 25%. Additionally, post-fire peak flows increase by 18-29% in watersheds burned with medium to high severity due to changes in soil properties. Conversely, a low-severity burn results in less than a 1% increase in post-fire peak flow. Furthermore, a high-severity fire causes a 38% reduction in the infiltration rate within the affected watershed during the first post-fire wet season. Hypothetical numerical experiments with varying precipitation regimes after a high-severity fire show that post-fire peak flows can increase by 1-29% due to fire-induced changes in soil hydraulic properties. This study highlights the importance of using fully distributed hydrologic models to quantify disturbance-feedback loops, which are essential for understanding the complexities brought about by spatial heterogeneity in post-fire landscapes.

ATS↗
NASA NTRS1997

Comparison of N2O Emissions from Soils at Three Temperate Agricultural Sites

Nitrous oxide (N2O) flux simulations by four models were compared with year-round field measurements from five temperate agricultural sites in three countries. The field sites included an unfertilized, semi-arid rangeland with low N2O fluxes in eastern Colorado, USA; two fertilizer treatments (urea and nitrate) on a fertilized grass ley cut for silage in Scotland; and two fertilized, cultivated crop fields in Germany where N2O loss during the winter was quite high. The models used were daily trace gas versions of the CENTURY model, DNDC, ExpertN, and the NASA-Ames version of the CASA model. These models included similar components (soil physics, decomposition, plant growth, and nitrogen transformations), but in some cases used very different algorithms for these processes. All models generated similar results for the general cycling of nitrogen through the agro-ecosystems, but simulated nitrogen trace gas fluxes were quite different. In most cases the simulated N20 fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N2O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N2), and ammonia (NH3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N2O emissions. All models simulated the general pattern of low background fluxes with high fluxes following fertilization at the Scottish sites, but they could not (or were not designed to) accurately capture the observed effects of different fertilizer types on N2O flux. None of the models were able to reliably generate large pulses of N2O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N2O fluxes for the dry site in Colorado. The US Trace Gas Network (TRAGNET) has provided a mechanism for this model and site intercomparison. Additional intercomparisons are needed with these and other models and additional data sets; these should include both tropical agro-ecosystems and new agricultural management techniques designed for sustainability.

Frolking, S. E.↗
DOE OSTI2014

Modeling adaptation of carbon use efficiency in microbial communities

In new microbial-biogeochemical models, microbial carbon use efficiency (CUE) is often assumed to decline with increasing temperature. Under this assumption, soil carbon losses under warming are small because microbial biomass declines. Yet there is also empirical evidence that CUE may adapt (i.e., become less sensitive) to warming, thereby mitigating negative effects on microbial biomass. To analyze potential mechanisms of CUE adaptation, I used two theoretical models to implement a tradeoff between microbial uptake rate and CUE. This rate-yield tradeoff is based on thermodynamic principles and suggests that microbes with greater investment in resource acquisition should have lower CUE. Microbial communities or individuals could adapt to warming by reducing investment in enzymes and uptake machinery. Consistent with this idea, a simple analytical model predicted that adaptation can offset 50% of the warming-induced decline in CUE. To assess the ecosystem implications of the rate-yield tradeoff, I quantified CUE adaptation in a spatially-structured simulation model with 100 microbial taxa and 12 soil carbon substrates. This model predicted much lower CUE adaptation, likely due to additional physiological and ecological constraints on microbes. In particular, specific resource acquisition traits are needed to maintain stoichiometric balance, and taxa with high CUE and low enzyme investment rely on low-yield, high-enzyme neighbors to catalyze substrate degradation. In contrast to published microbial models, simulations with greater CUE adaptation also showed greater carbon storage under warming. This pattern occurred because microbial communities with stronger CUE adaptation produced fewer degradative enzymes, despite increases in biomass. Thus, the rate-yield tradeoff prevents CUE adaptation from driving ecosystem carbon loss under climate warming.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022

From April 3 through April 6 of 2022, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Snow-on data was collected around two NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27 and Kougarok mm 64. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. This survey was conducted during expected peak snow cover at the end of the winter on the Seward Peninsula, and can be paired with data collected in 2021 during the snow-off campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021" (Singhania et al., 2023) (NGA270). This data package contains LiDAR point clouds (.las), Digital Elevation Models (.tif), and shapefiles of the .las tiling system (.shp). A project report detailing data collection details, GNSS stations and GNSS corrections, and processing steps is also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity

The long-term contribution of nitrification to nitrous oxide (N2O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of in situ N2O flux measurements with site-specific Michaelis-Menten kinetics of nitrification-derived N2O, we test the relative importance of nitrification-derived N2O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to in situ N2O fluxes was 13-17% in a conventionally fertilized annual cropping system, 27-42% in a low-input cover-cropped annual cropping system, and 52-63% in perennial systems including a late successional deciduous forest. Actual values are likely to be less than 10% of these values because of low N2O yields in cultured nitrifiers (typically 0.04 to 8% of NH3 oxidized) and competing sinks for available NH4+ in situ. Most nitrification-derived N2O was produced by ammonia oxidizing bacteria (AOB) rather than archaea (AOA), who appeared responsible for no more than 30% of nitrification-derived N2O production in all but one ecosystem. Although the proportion of nitrification-derived N2O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification-derived N2O (higher Vmax) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N2O in all ecosystems examined.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Diversity of Active Viral Infections within the Sphagnum Microbiome

Sphagnum -dominated peatlands play an important role in global carbon storage and represent significant sources of economic and ecological value. While recent efforts to describe microbial diversity and metabolic potential of the Sphagnum microbiome have demonstrated the importance of its microbial community, little is known about the viral constituents. We used metatranscriptomics to describe the diversity and activity of viruses infecting microbes within the Sphagnum peat bog. The vegetative portions of six Sphagnum plants were obtained from a peatland in northern Minnesota, and the total RNA was extracted and sequenced. Metatranscriptomes were assembled and contigs were screened for the presence of conserved virus marker genes. Using bacteriophage capsid protein gp23 as a marker for phage diversity, we identified 33 contigs representing undocumented phages that were active in the community at the time of sampling. Similarly, RNA-dependent RNA polymerase and the nucleocytoplasmic large DNA virus (NCLDV) major capsid protein were used as markers for single-stranded RNA (ssRNA) viruses and NCLDV, respectively. In total, 114 contigs were identified as originating from undescribed ssRNA viruses, 22 of which represent nearly complete genomes. An additional 64 contigs were identified as being from NCLDVs. Finally, 7 contigs were identified as putative virophage or polinton-like viruses. We developed co-occurrence networks with these markers in relation to the expression of potential-host housekeeping gene rpb1 to predict virus-host relationships, identifying 13 groups. Together, our approach offers new tools for the identification of virus diversity and interactions in understudied clades and suggests that viruses may play a considerable role in the ecology of the Sphagnum microbiome. Sphagnum -dominated peatlands play an important role in maintaining atmospheric carbon dioxide levels by modifying conditions in the surrounding soil to favor the growth of over that of other plant species. This lowers the rate of decomposition and facilitates the accumulation of fixed carbon in the form of partially decomposed biomass. The unique environment produced by Sphagnum enriches for the growth of a diverse microbial consortia that benefit from and support the moss's growth, while also maintaining the hostile soil conditions. While a growing body of research has begun to characterize the microbial groups that colonize Sphagnum , little is currently known about the ecological factors that constrain community structure and define ecosystem function. Top-down population control by viruses is almost completely undescribed. Furthermore, this study provides insight into the significant viral influence on the Sphagnum microbiome and identifies new potential model systems to study virus-host interactions in the peatland ecosystem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Observationally constrained analysis of sulfur cycle in the marine atmosphere with NASA ATom measurements and AeroCom model simulations

The atmospheric sulfur cycle plays a key role in air quality, climate, and ecosystems, such as pollution, radiative forcing, new particle formation, and acid rain. In this study, we compare the spatially and temporally resolved measurements from the NASA Atmospheric Tomography (ATom) mission with simulations from five AeroCom III models for four sulfur species (dimethyl sulfide (DMS), sulfur dioxide (SO 2 ), particulate methanesulfonate (MSA), and particulate sulfate (SO 4 )). We focus on remote regions over the Pacific, Atlantic, and Southern oceans from near the surface to ~12 km altitude range covering all four seasons. In general, the differences among model results can be greater than 1 order of magnitude. Comparing with observations, model-simulated SO 2 is generally low, whereas SO 4 is generally high. Simulated DMS concentrations near the sea surface exceed observed levels by a factor of 5 in most cases, suggesting potential overestimation of DMS emissions in all models. With GEOS model simulations of tagging emission from anthropogenic, biomass burning, volcanic, and oceanic sources, we find that anthropogenic emissions are the dominant source of sulfate aerosol (40 %–60 % of the total amount) in the ATom measurements at almost all altitudes, followed by volcanic emissions (18 %–32 %) and oceanic sources (16 %–32 %). Similar source contributions can also be derived at broad ocean basins and on monthly scales, indicating the representativeness of ATom measurements for global ocean. Our work presents the first assessment of AeroCom sulfur study using ATom measurements, providing directions for improving sulfate simulations, which remain the largest uncertainty in radiative forcing estimates in aerosol climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites

The Community Land Model (CLM) is an advanced process-based land surface model that simulates carbon, nitrogen, water vapor and energy exchanges between terrestrial ecosystems and the atmosphere at various spatial and temporal scales. We use observed carbon and water fluxes from five representative Chinese Terrestrial Ecosystem Flux Research Network (ChinaFLUX) eddy covariance tower sites to systematically evaluate the new version CLM4.5 and old version CLM4.0, and to generate insights that may inform future model developments. CLM4.5 underestimates the annual carbon sink at three forest sites and one alpine grassland site but overestimates the carbon sink of a semi-arid grassland site. The annual carbon sink underestimation for the deciduous-dominated forest site results from underestimated daytime carbon sequestration during summer and overestimated nighttime carbon emission during spring and autumn. Compared to CLM4.0, the bias of annual gross primary production (GPP) is reduced by 24% and 28% in CLM4.5 at two subtropical forest sites. However, CLM4.5 still presents a large positive bias in annual GPP. The improvement in net ecosystem exchange (NEE) is limited, although soil respiration bias decreases by 16%–43% at three forest sites. CLM4.5 simulates lower soil water content in the dry season than CLM4.0 at two grassland sites. Drier soils produce a significant drop in the leaf area index and in GPP and an increase in respiration for CLM4.5. The new fire parameterization approach in CLM4.5 causes excessive burning at the Changbaishan forest site, resulting in an unexpected underestimation of NEE, vegetation carbon, and soil organic carbon by 46%, 95%, and 87%, respectively. Altogether, our study reveals significant improvements achieved by CLM4.5 compared to CLM4.0, and suggests further developments on the parameterization of seasonal GPP and respiration, which will require a more effective representation of seasonal water conditions and the partitioning of net radiation between sensible and heat fluxes.

54 ENVIRONMENTAL SCIENCES↗