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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗
DOE OSTI2025

PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data

Abstract. Geological records of past environmental change provide crucial insights into long-term climate variability, trends, non-stationarity, and nonlinear feedback mechanisms. However, reconstructing spatiotemporal fields from these records is statistically challenging due to their sparse, indirect, and noisy nature. Here, we present PaleoSTeHM, a scalable and modern framework for spatiotemporal hierarchical modeling of paleo-environmental data. This framework enables the implementation of flexible statistical models that rigorously quantify spatial and temporal variability from geological data while clearly distinguishing measurement and inferential uncertainty from process variability. We illustrate its application by reconstructing temporal and spatiotemporal paleo-sea-level changes across multiple locations. Using various modeling and analysis choices, PaleoSTeHM demonstrates the impact of different methods on inference results and computational efficiency. Our results highlight the critical role of model selection in addressing specific paleo-environmental questions, showcasing the PaleoSTeHM framework's potential to enhance the robustness and transparency of paleo-environmental reconstructions.

58 GEOSCIENCES↗
NASA NTRS2014

Independent Verification of Mars-GRAM 2010 with Mars Climate Sounder Data

The Mars Global Reference Atmospheric Model (Mars-GRAM) is an engineering-level atmospheric model widely used for diverse mission and engineering applications. Applications of Mars-GRAM include systems design, performance analysis, and operations planning for aerobraking, entry, descent and landing, and aerocapture. Atmospheric influences on landing site selection and long-term mission conceptualization and development can also be addressed utilizing Mars-GRAM. Mars-GRAM's perturbation modeling capability is commonly used, in a Monte Carlo mode, to perform high-fidelity engineering end-to-end simulations for entry, descent, and landing. Mars-GRAM is an evolving software package resulting in improved accuracy and additional features. Mars-GRAM 2005 has been validated against Radio Science data, and both nadir and limb data from the Thermal Emission Spectrometer (TES). From the surface to 80 km altitude, Mars-GRAM is based on the NASA Ames Mars General Circulation Model (MGCM). Above 80 km, Mars-GRAM is based on the University of Michigan Mars Thermospheric General Circulation Model (MTGCM). The most recent release of Mars-GRAM 2010 includes an update to Fortran 90/95 and the addition of adjustment factors. These adjustment factors are applied to the input data from the MGCM and the MTGCM for the mapping year 0 user-controlled dust case. The adjustment factors are expressed as a function of height (z), latitude and areocentric solar longitude (Ls).

Justh, Hilary L.↗
DOE OSTI2020

NGEE Arctic Plant Traits: Plant Community Composition, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016

This dataset reports the characteristics of the dominant vegetation communities at the NGEE Arctic Kougarok field site, Kougarok Road Mile Marker 64 on the Seward Peninsula. Selected plots were surveyed from 18-23 July 2016. Environmental data (e.g., elevation, slope, soil moisture regime, disturbance type and degree, mean canopy height, etc.) were also recorded for each plot. Elevation measurements were updated 2020-08-18. All plant species (vascular plants, bryophytes and lichens) were recorded along with their percent cover in the plots as determined visually by the lead author. Plots were chosen subjectively in areas of homogeneous and representative vegetation and varied in size from 1-25 m2 depending on canopy structure and height. The site is an east-facing hillslope with vegetation that varies from the summit to the toeslope. The 30 total plots were selected as five replicate plots sampled from each of the six identified habitat types: 1) non-acidic mountain complex at the hillcrest, 2) dwarf-shrub lichen tundra on the shoulder, 3) alder shrublands predominantly on an elevational band across the backslope, 4) willow-birch tundra on the upper backslope, 5) tussock tundra on the footslope in inter-water tracks, and 6) tussock tundra mixed with willow-birch tundra or alder savanna in poor developed water tracks on the footslope. This dataset is comprised of two comma-separated (*.csv) files containing species and environmental data for the plant community composition plots. It also contains seven zipped folders of plot photographs, one map showing plot locations (*.pdf), one blank datasheet with the key to codes used in the field (*.pdf), select photos organized in a (*.pdf), and one User file (*.pdf). 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↗
NASA NTRS2011

Simultaneous Retrieval of Aerosol and Cloud Properties During the MILAGRO Field Campaign

Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. Recently, passive remote sensing instruments have been developed that have the potential to retrieve both cloud and aerosol properties using polarimetric, multiple view angle, and multi spectral observations, and therefore determine DCF from aerosols above clouds. One such instrument is the Research Scanning Polarimeter (RSP), an airborne prototype of a sensor on the NASA Glory satellite, which unfortunately failed to reach orbit during its launch in March of 2011. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On 13 March, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution parameters and the cloud droplet size distribution parameters to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this study in the context of future systematic scanning polarimeter observations, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of (0.555 m).

Knobelspiesse, K.↗
DOE OSTI2020

Quantifying the effects of urbanization on floods in a changing environment to promote water security — A case study of two adjacent basins in Texas

The increased occurrence of flood events resulting from urbanization and global climate change is a great threat to water security. To systematically evaluate the impacts of urbanization on floods, here we applied a paired catchments approach to two adjacent river basins in south-central Texas — the San Antonio River Basin (SARB), with fast urbanization; and the Guadalupe River Basin (GRB), with little land cover change. A physics-based distributed hydrological model — the Distributed Hydrology Soil Vegetation Model, embedded with a multi-purpose reservoir module (DHSVM-Res) — was used to simulate streamflow and reservoir storage. The simulations were conducted under different land cover scenarios, including a newly developed continuous land cover series (CLCS). Holistic analyses were then conducted for the paired basins using three methods: analyzing the selected flood events, detecting change points (CP) of monthly floods, and testing the elasticity of long-term flood regimes. The results suggest that: (1) urbanization may reduce lag time and elevate flood peaks significantly by 3–30% in our study area; (2) when there is little land cover change, changing climate is the major driver of variations in the monthly maximum streamflow (MMS); (3) fast urbanization can amplify streamflow variability, increase MMS significantly, and thus alter the timing of CP; and (4) the mean MMS of observed streamflow in the SARB has increased by as much as 75.7% from the pre-CP to post-CP periods. This comprehensive study fills in a gap in our current understanding of the isolated impacts of urbanization on flooding and is expected to support future explorations of anthropogenic influences on floods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Invasions eliminate the legacy effects of substrate history on microbial nitrogen cycling

Abstract Changes in substrate quality driven by climate, land use, or other forms of global change may represent a strong selective force on microbial communities. Invasion of new taxa into a community through dispersal, evolution, or recolonization could impact the outcome of this environmental selection. Here, we simulated substrate change with a trait‐based model of microbial litter decomposition (DEMENTpy) to assess the legacy effects of past substrate quality and the impact of selection by a new substrate on community decomposition activity. Simulations were run with different levels of invasion, including invasion from communities long‐adapted to the new substrate. Legacy effects were evident with substrate change for native communities differing in composition. Protein was the only substrate that exerted a strong enough selective force to affect community composition. Legacy effects disappeared when invaders came from substrates similar to the new substrate. Together, our simulations demonstrate that substrate quality changes associated with global change can lead to legacy effects on substrate degradation. In decomposing plant litter, such legacy effects can occur if substrate inputs shift to higher protein content and if invasion is low.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Instrumentation for Examining Microbial Response to Changes In Environmental Pressures

The Automated Adaptive Directed Evolution Chamber (AADEC) is a device that allows operators to generate a micro-scale analog of real world systems that can be used to model the local-scale effects of climate change on microbial ecosystems. The AADEC uses an artificial environment to expose cultures of micro-organisms to environmental pressures, such as UV-C radiation, chemical toxins, and temperature. The AADEC autonomously exposes micro-organisms to selection pressures. This improves upon standard manual laboratory techniques: the process can take place over a longer period of time, involve more stressors, implement real-time adjustments based on the state of the population, and minimize the risk of contamination. We currently use UV-C radiation as the main selection pressure, UV-C is well studied both for its cell and DNA damaging effects as a type of selection pressure and for its related effectiveness as a mutagen; having these functions united makes it a good choice for a proof of concept. The AADEC roadmap includes expansion to different selection pressures, including heavy metal toxicity, temperature, and other forms of radiation.The AADEC uses closed-loop control to feedback the current state of the culture to the AADEC controller that modifies selection pressure intensity during experimentation, in this case culture density and growth rate. Culture density and growth rate are determined by measuring the optical density of the culture using 600 nm light. An array of 600 nm LEDs illuminate the culture and photodiodes are used to measure the shadow on the opposite side of the chamber.Previous experiments showed that we can produce a million fold increase to UV-C radiation over seven iterations. The most recent implements a microfluidic system that can expose cultures to multiple different selection pressures, perform non-survival based selection, and autonomously perform hundreds of exposure cycles. A scalable pump system gives the ability to pump in various different growth media to individual cultures and introduce chemical toxins during experimentation; AADEC can perform freeze and thaw cycles. We improved our baseline characterization by building a custom UV-C exposure hood, a shutter operates on a preset timer allowing the user to set exposure intensity consistently for multiple iterations.

Environment↗
DOE OSTI2020

Design screening and analysis of gas-fired ammonia-based chemisorption heat pumps for space heating in cold climate

Thermally-driven ammonia-based chemisorption heat pumps (CSHP) have the potential to provide high-efficiency space heating in cold climates. Using the reversible chemical bond between sorbent salt and ammonia, CSHP thermochemically pumps heat from the cold ambient to the end-uses of space heating at 50 °C. The heating coefficient of performance (COP) of a CSHP largely depends on the selection of the sorbent salts, cycle configuration, and the system operation. This study uses a thermodynamic model to investigate the performance of six CSHP system configurations, including four single-effect and two double-effect cycles. The feasibility and performance of 121 available NH 3 /salt reactions are studied for each configuration. The thermal COP of the cycles and the primary energy COP of the gas-fired CSHP systems are evaluated assuming 50 °C supply temperature for building space heating and the optimal system designs are identified. The highest thermal COP for single-effect and double-effect cycles under -25 °C ambient temperatures are predicted to be 1.22 and 1.57, respectively. The corresponding primary energy COPs are above 1.0 and 1.15, which are 30% higher than condensing furnaces and is sustained into the same cold temperatures.

42 ENGINEERING↗
NASA NTRS2010

Comparison of Satellite-Derived TOA Shortwave Clear-Sky Fluxes to Estimates from GCM Simulations Constrained by Satellite Observations of Land Surface Characteristics

Clear-sky, upwelling shortwave flux at the top of the atmosphere (S(sub TOA raised arrow)), simulated using the atmospheric and land model components of the Community Climate System Model 3 (CCSM3), is compared to corresponding observational estimates from the Clouds and Earth's Radiant Energy System (CERES) sensor. Improvements resulting from the use of land surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the simulations are also examined. Compared to CERES observations, CCSM3 overestimates global, annual averaged S(sub TOA raised arrow) over both land and oceans. However, regionally, CCSM3 overestimates S(sub TOA raised arrow) over some land and ocean areas while underestimating it over other sites. CCSM3 underestimates S(sub TOA raised arrow) over the Saharan and Arabian Deserts and substantial differences exist between CERES observations and CCSM3 over agricultural areas. Over selected sites, after using groundbased observations to remove systematic biases that exist in CCSM computation of S(sub TOA raised arrow), it is found that use of MODIS albedo improves the simulation of S(sub TOA raised arrow). Inability of coarse resolution CCSM3 simulation to resolve spatial heterogeneity of snowfall over high altitude sites such as the Tibetan Plateau causes overestimation of S(sub TOA raised arrow) in these areas. Discrepancies also exist in the simulation of S(sub TOA raised arrow) over ocean areas as CCSM3 does not account for the effect of wind speed on ocean surface albedo. This study shows that the radiative energy budget at the TOA is improved through the use of MODIS albedo in Global Climate Models.

Anantharaj, Valentine G.↗
DOE OSTI2022

Empirical relationships between environmental factors and soil organic carbon produce comparable prediction accuracy as the Machine Learning

Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon-climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental US, and two Machine Learning approaches [Random Forest (RF) and Generalized Additive Modeling (GAM)] to (1) select important environmental predictors of SOC stocks, (2) derive empirical relationships between environmental factors and SOC stocks, and (3) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy as the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Using a Web Browser for Environmental and Climate Change Studies

A new web browser for viewing and manipulating meteorological data sets is located on a web server at NASA, Langley Research Center. The browser uses a live access server (LAS) developed by the Thermal Modeling and Analysis Project at NOAA's Pacific Marine Environmental Laboratory. LAS allows researchers to interact directly with the data to view, select, and subset the data in terms of location (latitude, longitude) and time such as day, month, or year. In addition, LAS can compare two data sets and can perform averages and variances, LAS is used here to show how it functions as an internet/web browser for use by the scientific and educational community. In particular its versatility in displaying and manipulating data sets of atmospheric measurements in the earth's radiation budget (ERB) or energy balance, which includes measurements of absorbed solar radiation, reflected shortwave radiation (RSW), thermal outgoing longwave radiation (OLR), and net radiation is demonstrated. These measurements are from the Clouds and the Earth's Radiant Energy System (CERES) experiment and the surface radiation budget (SRB) experiment.

Bess, T. Dale↗
NASA NTRS2002

Using a Web Browser for Environmental and Climate Change Studies

A new web browser for viewing and manipulating meteorological data sets is located on a web server at NASA, Langley Research Center. The browser uses a live access server (LAS) developed by the Thermal Modeling and Analysis Project at NOAA's Pacific Marine Environmental Laboratory. LAS allows researchers to interact directly with the data to view, select, and subset the data in terms of location (latitude, longitude) and time such as day, month, or year. In addition, LAS can compare two data sets and can perform averages and variances, LAS is used here to show how it functions as an internet/web browser for use by the scientific and educational community. In particular its versatility in displaying and manipulating data sets of atmospheric measurements in the earth s radiation budget (ERB) or energy balance, which includes measurements of absorbed solar radiation, reflected shortwave radiation (RSW), thermal outgoing longwave radiation (OLR), and net radiation is demonstrated. These measurements are from the Clouds and the Earth s Radiant Energy System (CERES) experiment and the surface radiation budget (SRB) experiment.

Bess, T. Dale↗
NASA NTRS2009

Developing a Model-based Capability to Analyze Requirements for the Climate Observing System

Models are foundational for estimating states of the earth's climate system, both as tools to extrapolate information in time and space, and as observation 'operators' used to relate what is analyzed and predicted to what is observed. Expanding the simulation approach further, observing system simulation experiments (OSSEs) are designed to mimic the complete process of analyzing the climate state by replacing real observations with entirely simulated ones determined from a model-based depiction of nature. OSSEs provide a framework to 'fly' simulated satellite instruments through a synthetic atmosphere and investigate the trade-spaces of measurements for various satellite configurations and sampling strategies, and assess their measurement impact on modeling and forecasting capabilities. Such a tool is a crucial but as yet unfulfilled need for future mission selection and design. The components of a state-of-the-art OSSE system are being assembled at the Global Modeling and Assimilation Office (GMAO, Code 610.1) at NASA/GSFC, leveraging on the GMAO's existing modeling and data assimilation infrastructure for numerical weather prediction (NWP). The OSSE framework is based on the GMAO's Goddard Earth Observing System atmospheric general circulation model, version 5 (GEOS-5) and the Gridpoint Statistical Interpolation (GSI) observational analysis scheme, combined with the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model developed by the Atmospheric Chemistry and Dynamics Branch (Code 613.3). This system is an evolving, key component of Goddard's planned development of an Integrated Earth System Analysis (IESA) capability, which will bring together into a single, fully interactive system Goddard's modeling and assimilation efforts in atmosphere, ocean and chemistry and aerosols to provide a comprehensive analysis and prediction system for weather and climate In addition to providing a state-of-the-art capability for assimilating current observation types, GEOS-5, and the future IESA, provide the capability to identify the need for, and assess the potential impact of, future observing systems under consideration for improving weather and climate prediction.

Gelaro, Ronald↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying microbial control of soil organic matter dynamics at macrosystem scales

Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. Here, when employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Terrestrial carbon dynamics in an era of increasing wildfire

In an increasingly flammable world, wildfire is altering the terrestrial carbon balance. However, the degree to which novel wildfire regimes disrupt biological function remains unclear. Here, we synthesize the current understanding of above and below ground processes that govern carbon loss and recovery across diverse ecosystems. In this study, we find that intensifying wildfire regimes are increasingly exceeding biological thresholds of resilience, causing ecosystems to convert to a lower carbon-carrying capacity. Growing evidence suggests that plants compensate for fire damage by allocating carbon below ground to access nutrients released by fire, while wildfire selects for microbial communities with rapid growth rates and the ability to metabolize pyrolyzed carbon. Determining controls on carbon dynamics following wildfire requires integration of experimental and modeling frameworks across scales and ecosystems. Fire severity is expected to increase as a result of warming. This will potentially amplify climate change due to its impact on the carbon cycle. This Review discusses ecosystem carbon loss and recovery following wildfire, and highlights where further work is needed to inform model predictions.

54 ENVIRONMENTAL SCIENCES↗