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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Normalized Quantitative PCR Measurements as Predictors for Ethene Formation at Sites Impacted with Chlorinated Ethenes

Quantitative PCR (qPCR) targeting Dehalococcoides mccartyi (Dhc) biomarker genes supports effective management at sites impacted with chlorinated ethenes. To establish correlations between Dhc biomarker gene abundances and ethene formation (i.e., detoxification), 859 groundwater samples representing 62 sites undergoing monitored natural attenuation or enhanced remediation were analyzed. Dhc 16S rRNA genes and the vinyl chloride (VC) reductive dehalogenase genes bvcA and vcrA were detected in 88% and 61% of samples, respectively, from wells with ethene. Dhc 16S rRNA, bvcA , vcrA , and tceA (implicated in cometabolic reductive VC dechlorination) gene abundances all positively correlated with ethene formation. Significantly greater ethene concentrations were observed when Dhc 16S rRNA gene and VC RDase gene abundances exceeded 10 7 and 10 6 copies L –1 , respectively, and when Dhc 16S rRNA- and bvcA + vcrA -to-total bacterial 16S rRNA gene ratios exceeded 0.1%. Dhc 16S rRNA gene-to- vcrA/bvcA ratios near unity also indicated elevated ethene; yet, no increased ethene was observed in 19 wells where vcrA and/or bvcA gene copy numbers exceeded Dhc cell numbers 10- to 10 000-fold. Approximately one-third of samples with detectable ethene lacked bvcA , vcrA , and tceA , implying that comprehensive understanding of VC detoxification biomarkers has not been achieved. Although the current biomarker suite is incomplete, the data analysis corroborates the value of the available Dhc DNA biomarkers for prognostic and diagnostic groundwater monitoring at sites impacted with chlorinated ethenes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data

The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart (https://pmart.labworks.org) is an interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Downscaling Aerosols and the Impact of Neglected Subgrid Processes on Direct Aerosol Radiative Forcing for a Representative Global Climate Model Grid Spacing

Recent improvements to many global climate models include detailed, prognostic aerosol calculations intended to better reproduce the observed climate. However, the trace gas and aerosol fields are treated at the grid-cell scale with no attempt to account for sub-grid impacts on the aerosol fields. This paper begins to quantify the error introduced by the neglected sub-grid variability for the shortwave aerosol radiative forcing for a representative climate model grid spacing of 75 km. An analysis of the value added in downscaling aerosol fields is also presented to give context to the WRF-Chem simulations used for the sub-grid analysis. We found that 1) the impact of neglected sub-grid variability on the aerosol radiative forcing is strongest in regions of complex topography and complicated flow patterns, and 2) scale-induced differences in emissions contribute strongly to the impact of neglected sub-grid processes on the aerosol radiative forcing. The two of these effects together, when simulated at 75 km vs. 3 km in WRF-Chem, result in an average daytime mean bias of over 30% error in top-of-atmosphere shortwave aerosol radiative forcing for a large percentage of central Mexico during the MILAGRO field campaign.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Simulating coupled carbon and nitrogen dynamics following mountain pine beetle outbreaks in the western United States

Insect outbreaks are major ecosystem disturbances, affecting a similar area as forest fires annually across North America. Tree mortality caused by epidemics of bark beetles alters carbon cycling in the first several years following the disturbance by reducing stand-level primary production and increasing decomposition rates. The few studies of biogeochemical cycling following outbreaks have shown a range of impacts from small responses of net carbon fluxes in the first several years after a severe outbreak to large forest areas that are sources of carbon to the atmosphere for decades. To gain more understanding about causes of this range of responses, we used an ecosystem model to assess impacts of different bark beetle outbreak conditions on coupled carbon and nitrogen cycling. We modified the Community Land Model with prognostic carbon and nitrogen to include prescribed bark beetle outbreaks. We then compared control simulations (without a bark beetle outbreak) to simulations with various mortality severity, durations of outbreak, and snagfall dynamics to quantify the range of carbon flux responses and recovery rates of net ecosystem exchange to a range of realistic outbreak conditions. Prescribed mortality by beetles reduced leaf area and thus productivity. Gross primary productivity decreased by as much as 80% for a severe outbreak (95% mortality) and by 10% for less severe outbreaks (25% mortality). Soil mineral nitrogen dynamics (immobilization and plant uptake) were important in governing post-outbreak productivity, and were strongly modulated by carbon inputs to the soil from killed trees. Initial increases in heterotrophic respiration caused by a pulse of labile carbon from roots were followed by a slight reduction (from pre-snagfall reduced inputs), then a secondary increase (from inputs due to snagfall). Secondary increases in heterotrophic respiration were largest for simulated windthrow of snags after a prescribed snagfall delay period. Net ecosystem productivity recovered within 40 years for all simulations, with the largest increases in the first 10 years. Our simulations illustrate that, given the large variability in bark beetle outbreak conditions, a wide range of responses in carbon and nitrogen dynamics can occur. The fraction of trees killed, timing of snagfall, snagfall rate, and management decisions as to whether or not to remove snags for harvesting or for fire prevention will have a major impact on post-outbreak carbon fluxes up to 100 years following an outbreak.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States

Fine particulate matter (PM2.5) from U.S. anthropogenic sources is decreasing. However, previous studies have predicted that PM2.5 emissions from wildfires will increase in the midcentury to next century, potentially offsetting improvements gained by continued reductions in anthropogenic emissions. Therefore, some regions could experience worse air quality, degraded visibility, and increases in population-level exposure. We use global climate model simulations to estimate the impacts of changing fire emissions on air quality, visibility, and premature deaths in the middle and late 21st century. We find that PM2.5 concentrations will decrease overall in the contiguous United States (CONUS) due to decreasing anthropogenic emissions (total PM2.5 decreases by 3% in Representative Concentration Pathway [RCP] 8.5 and 34% in RCP4.5 by 2100), but increasing fire-related PM2.5 (fire-related PM2.5 increases by 55% in RCP4.5 and 190% in RCP8.5 by 2100) offsets these benefits and causes increases in total PM2.5 in some regions. We predict that the average visibility will improve across the CONUS, but fire-related PM2.5 will reduce visibility on the worst days in western and southeastern U.S. regions. We estimate that the number of deaths attributable to total PM2.5 will decrease in both the RCP4.5 and RCP8.5 scenarios (from 6% to 4–5%), but the absolute number of premature deaths attributable to fire-related PM2.5 will double compared to early 21st century. We provide the first estimates of future smoke health and visibility impacts using a prognostic land-fire model. Our results suggest the importance of using realistic fire emissions in future air quality projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Do We Need to Parameterize Mesoscale Convective Organization to Mitigate the MJO-Mean State Trade-Off?

Modifications in cumulus parameterizations that improve Madden-Julian oscillation (MJO) simulation tend to degrade the mean state—known as the MJO-mean state trade-off. The impacts of parameterizing mesoscale convective organization on the relationship between the MJO and the mean state simulation fidelity are examined. A series of experiments are made with a general circulation model that parameterizes the mesoscale convective organization and simulates well both the MJO and the mean state. In the control simulation, a prognostic nondimensional variable (Ω) represents the degree of convective organization. In order to examine the effect of the parameterized convective organization, fixed Ω values are imposed in a series of constrained experiments. The fixed-Ω simulations show a negative relationship between the MJO and the mean state simulation fidelity. The control simulation is deviated from the negative relationship, suggesting the parameterized mesoscale convective organization helps general circulation models to mitigate the MJO-mean state trade-off.

58 GEOSCIENCES↗
DOE OSTI2019

Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer

Deep neural networks (DNNs) are implemented in Super-Parameterized Energy Exascale Earth System Model (SP-E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90–95% at a cost of 8–10 times cheaper than the original radiation parameterization. Here, a comparison of time-averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations.

58 GEOSCIENCES↗
DOE OSTI2018

Changes in Wood Biomass and Crop Yields in Response to Projected CO 2 , O 3 , Nitrogen Deposition, and Climate

As the world's population increases, so will the demand for food and timber resources. Though carbon dioxide (CO 2 ) fertilization and, to a lesser extent, nitrogen (N) deposition are expected to increase future resource production, changes in ozone (O 3 ) and climate have the potential to decrease production due to increased phytotoxic damage, drought, and heat stress. To determine how crop and timber production may change in the future, we use the Community Land Model version 4.5 with prognostic crops to simulate responses of wood biomass and crop yields to CO 2 , O 3 , N deposition, and climate under Representative Concentration Pathway 8.5 forcings. Generally, rising CO 2 increases wood biomass and crop yields, while projected climate change causes decreases. Small projected changes in O 3 and N deposition do not strongly affect yields, though additional research is needed on future O 3 and N deposition trends and impacts. By the end of the 21st century, global wood biomass increases by ~16% due to the dominating impact of CO 2 . The positive effect of CO 2 on future crop yields is muted by the negative impacts of climate, with a ~5% net global increase. Future projections suggest that rice and wheat yields typically increase under the combination of future forcings, whereas soy and corn yields are regionally variable. While short-term resource management strategies can benefit from planting heat-tolerant species and cultivars, technological advances and intensification, among other management strategies not included here, must be employed to meet the future demand for these resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Representing Nitrogen, Phosphorus, and Carbon Interactions in the E3SM Land Model: Development and Global Benchmarking

Over the past several decades, the land modeling community has recognized the importance of nutrient regulation on the global terrestrial carbon cycle. Implementations of nutrient limitation in land models are diverse, varying from applying simple empirical down–regulation of potential gross primary productivity under nutrient deficit conditions to more mechanistic treatments. In this study, we introduce a new approach to model multinutrient (nitrogen [N] and phosphorus [P]) limitations in the Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELMv1–ECA). The development is grounded on (1) advances in representing multiple–consumer, multiple–nutrient competition; (2) a generic dynamic allocation scheme based on water, N, P, and light availability; (3) flexible plant CNP stoichiometry; (4) prognostic treatment of N and P constraints on several carbon cycle processes; and (5) global data sets of plant physiological traits. Through benchmarking the model against best knowledge of global plant and soil carbon pools and fluxes, we show that our implementation of nutrient constraints on the present–day carbon cycle is robust at the global scale. Compared with predecessor versions, ELMv1–ECA better predicts global–scale gross primary productivity, ecosystem respiration, leaf area index, vegetation biomass, soil carbon stocks, evapotranspiration, N2O emissions, and NO3– leaching. Factorial experiments imply that representing the phosphorus cycle improves modeled carbon fluxes, while considering dynamic allocation improves modeled carbon stock density. We also illustrate the value of using the International Land Model Benchmarking (ILAMB) package to evaluate and document performance during model development.

58 GEOSCIENCES↗
DOE OSTI2019

The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

58 GEOSCIENCES↗
DOE OSTI2019

Climate Forcing and Trends of Organic Aerosols in the Community Earth System Model (CESM2)

The Community Earth System Model version 2 (CESM2) includes three main atmospheric configurations: the Community Atmosphere Model version 6 (CAM6) with simplified chemistry and a simplified organic aerosol (OA) scheme, CAM6 with comprehensive tropospheric and stratospheric chemistry representation (CAM6-chem), and the Whole Atmosphere Community Climate Model version 6 (WACCM6). Both, CAM6-chem and WACCM6 include a more comprehensive secondary organic aerosols (SOA) approach using the volatility basis set (vbs) scheme and prognostic stratospheric aerosols. This paper describes the different OA schemes available in the different atmospheric configurations of CESM2 and discusses differences in aerosol burden and resulting climate forcings. Derived OA burden and trends differ due to differences in OA formation using the different approaches. Regional differences in Aerosol Optical Depth with larger values using the comprehensive approach occur over SOA source regions. Stronger increasing SOA trends between 1960 and 2015 in WACCM6 compared to CAM6 are due to increasing biogenic emissions aligned with increasing surface temperatures. Using the comprehensive SOA approach further leads to improved comparisons to aircraft observations and SOA formation of ˜143 Tg/yr. We further use WACCM6 to identify source contributions of OA from biogenic, fossil fuel, and biomass burning emissions, to quantify SOA amounts and trends from these sources. Increasing SOA trends between 1960 and 2015 are the result of increasing biogenic emissions aligned with increasing surface temperatures. Biogenic emissions are at least two thirds of the total SOA burden. In addition, SOA source contributions from fossil fuel emissions become more important, with largest values over Southeast Asia. The estimated total anthropogenic forcing of OA in WACCM6 for 1995–2010 conditions is -0.43 W/m2, mostly from the aerosol direct effect.

Tilmes, S↗
DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Binomial Stochastic Framework for Efficiently Modeling Discrete Statistics of Convective Populations

Abstract Understanding the coupling between convective clouds and the general circulation, as well as addressing the gray zone problem in convective parameterization, requires insight into the genesis and maintenance of spatial patterns in cumulus cloud populations. In this study, a simple toy model for recreating populations of interacting convective objects as distributed over a two‐dimensional Eulerian grid is formulated to this purpose. Key elements at the foundation of the model include i) a fully discrete formulation for capturing discrete behavior in convective properties at small population sample sizes, ii) object age‐dependence for representing life‐cycle effects, and iii) a prognostic number budget allowing for object interactions and co‐existence of multiple species. A primary goal is to optimize the computational efficiency of this system. To this purpose the object birth rate is represented stochastically through a spatially aware Bernoulli process. The same binomial stochastic operator is applied to horizontal advection of objects, conserving discreteness in object number. The applicability to atmospheric convection as well as behavior implied by the formulation is assessed. Various simple applications of the BiOMi model (Binomial Objects on Microgrids) are explored, suggesting that important convective behavior can be captured at low computational cost. This includes i) subsampling effects and associated powerlaw scaling in the convective gray zone, ii) stochastic predator‐prey behavior, iii) the downscale turbulent energy cascade, and iv) simple forms of spatial organization and convective memory. Consequences and opportunities for convective parameterization in next‐generation weather and climate models are discussed.

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

Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH 4 Profiles

Abstract The Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH 4 source. CH 4 emissions from wetlands have recently become of heightened interest because global atmospheric 13 CH 4 data indicate they may contribute to the resumption of atmospheric CH 4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH 4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer‐free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH 4 yr −1 (maximum flux ∼0.4 gCH 4 m −2 d −1 ) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH 4 yr −1 . Compared to recent estimates for Amazonia (∼41 ± 3 TgCH 4 yr −1 , maximum flux ∼0.3 gCH 4 m −2 d −1 ) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH 4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH 4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH 4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer‐term trends in CH 4 emissions and the potential for future wetlands CH 4 emissions climate feedbacks.

Gloor, M.↗
DOE OSTI2021

Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought

In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Simulations With the Marine Biogeochemistry Library (MARBL)

Abstract The Marine Biogeochemistry Library (MARBL) is a prognostic ocean biogeochemistry model that simulates marine ecosystem dynamics and the coupled cycles of carbon, nitrogen, phosphorus, iron, silicon, and oxygen. MARBL is a component of the Community Earth System Model (CESM); it supports flexible ecosystem configuration of multiple phytoplankton and zooplankton functional types; it is also portable, designed to interface with multiple ocean circulation models. Here, we present scientific documentation of MARBL, describe its configuration in CESM2 experiments included in the Coupled Model Intercomparison Project version 6 (CMIP6), and evaluate its performance against a number of observational data sets. The model simulates present‐day air‐sea CO 2 flux and many aspects of the carbon cycle in good agreement with observations. However, the simulated integrated uptake of anthropogenic CO 2 is weak, which we link to poor thermocline ventilation, a feature evident in simulated chlorofluorocarbon distributions. This also contributes to larger‐than‐observed oxygen minimum zones. Moreover, radiocarbon distributions show that the simulated circulation in the deep North Pacific is extremely sluggish, yielding extensive oxygen depletion and nutrient trapping at depth. Surface macronutrient biases are generally positive at low latitudes and negative at high latitudes. CESM2 simulates globally integrated net primary production (NPP) of 48 Pg C yr −1 and particulate export flux at 100 m of 7.1 Pg C yr −1 . The impacts of climate change include an increase in globally integrated NPP, but substantial declines in the North Atlantic. Particulate export is projected to decline globally, attributable to decreasing export efficiency associated with changes in phytoplankton community composition.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Incorporating Microtopography in a Land Surface Model and Quantifying the Effect on the Carbon Cycle

Abstract Northern peatlands are a terrestrial carbon store, with an annual sink of 0.1 Pg C yr −1 and a total storage estimate of 547 Pg C. Northern peatlands are also major contributors of atmospheric methane. Most land surface models do not accurately represent peatland carbon emissions, partly because they do not represent the hydrologic cycle and/or microtopography adequately. Interactions between water table depth and microtopography in peatlands influence decomposition and modulate CO 2 and CH 4 fluxes. A modified version of the land surface component of the Energy Exascale Earth System Model, was recently created to represent the microtopography and hydrology of a raised dome bog in northern Minnesota, USA. In this study, three microtopographic parameters are analyzed in the modified version: hummock height, hummock‐hollow spacing, and percent hollow. Terrestrial laser scanning observations are used to set uncertainty bounds for these parameters. Our model experiment results suggest that carbon‐related quantities of interest (QOI) were typically the most sensitive to hummock height, and those QOI (especially net ecosystem exchange, NEE) were sensitive to interactions between parameters. Furthermore, NEE was most relatively influenced by microtopographic parameters in the model, varying by 35%. We found that increasing hummock height resulted in more C being stored in plant tissue and less in soil organic matter. This coincided with decreases in Sphagnum and increases in Picea and shrub net primary production. These results suggest that future studies may consider extending prognostic capabilities of carbon cycling by incorporating hummock hollow microtopography into earth system models.

58 GEOSCIENCES↗
DOE OSTI2023

Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft-derived metric is bias-corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 17 Pg C, heterotrophic respiration to be 25 ± 13 Pg C, and net primary productivity to be 28 ± 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗