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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Key predictors of soil organic matter vulnerability to mineralization differ with depth at a continental scale

Abstract Soil organic matter (SOM) is the largest terrestrial pool of organic carbon, and potential carbon-climate feedbacks involving SOM decomposition could exacerbate anthropogenic climate change. However, our understanding of the controls on SOM mineralization is still incomplete, and as such, our ability to predict carbon-climate feedbacks is limited. To improve our understanding of controls on SOM decomposition, A and upper B horizon soil samples from 26 National Ecological Observatory Network (NEON) sites spanning the conterminous U.S. were incubated for 52 weeks under conditions representing site-specific mean summer temperature and sample-specific field capacity (−33 kPa) water potential. Cumulative carbon dioxide respired was periodically measured and normalized by soil organic C content to calculate cumulative specific respiration (CSR), a metric of SOM vulnerability to mineralization. The Boruta algorithm, a feature selection algorithm, was used to select important predictors of CSR from 159 variables. A diverse suite of predictors was selected (12 for A horizons, 7 for B horizons) with predictors falling into three categories corresponding to SOM chemistry, reactive Fe and Al phases, and site moisture availability. The relationship between SOM chemistry predictors and CSR was complex, while sites that had greater concentrations of reactive Fe and Al phases or were wetter had lower CSR. Only three predictors were selected for both horizon types, suggesting dominant controls on SOM decomposition differ by horizon. Our findings contribute to the emerging consensus that a broad array of controls regulates SOM decomposition at large scales and highlight the need to consider changing controls with depth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Phylogenetic conservatism of mycoparasitism and its contribution to pathogen antagonism

Abstract Closely related species are expected to have similar functional traits due to shared ancestry and phylogenetic inertia. However, few tests of this hypothesis are available for plant‐associated fungal symbionts. Fungal leaf endophytes occur in all land plants and can protect their host plant from disease by a variety of mechanisms, including by parasitizing pathogens (e.g., mycoparasitism). Here, we tested whether phylogenetic relatedness among species of Cladosporium , a widespread genus that includes mycoparasitic species, predicts the effect of this endophyte on the severity of leaf rust disease. First, we used congruence among different marker sequences (i.e., genealogical concordance phylogenetic species recognition criterion) to delimit species of Cladosporium . Next, in a controlled experiment, we quantified both mycoparasitism and disease modification for the selected Cladosporium species. We identified 17 species of Cladosporium ; all the species reduced rust disease severity in our experiment. Cladosporium phylogeny was a significant predictor of mycoparasitism. However, we did not observe a phylogenetic effect on disease severity overall, indicating that other mechanism/s operating independently of shared ancestry also contributed to endophyte effects on disease severity. Indeed, a second experiment showed that Cladosporium endophyte exudates (no live organism) from divergent species groups equally reduced disease severity. Our results reveal that multiple mechanisms contribute to the protective effects of an endophyte against a plant pathogen, but not all traits underlying these mechanisms are phylogenetically conserved.

Barge, Edward G.↗
DOE OSTI2026

A fresh perspective on the role of band bending, and related contributors, in light-driven production of electricity and chemicals

It is widely known that semiconductor-based solar energy conversion could power our planet. This is in part because high-quality semiconductor structures are unrivalled in their ability to separate photogenerated electrons and holes. One effective approach to achieving this photoinduced charge separation relies on a phenomenon known as “band bending”. But details to justify why band bending results in photoinduced charge separation are more complex than often appreciated. This underappreciation is an impediment to the rational, hypothesis-driven design of next-generation approaches to solar energy conversion. Herein we show, by means of derivations rooted in physical chemistry, that several phenomena – not just band bending – can facilitate photoinduced charge separation, and that each is influenced by nonequilibrium species concentration and a parameter, such as diffusion coefficient or rate coefficient, that introduces dynamics. To help visualize the impact of each phenomenon, we introduce plots that depict their contributions as free energy, force, flux, force constant, and rate. We reveal that spatial dopant distributions that define band bending are predictors of initial photogenerated species transport rates. But charge separation alone does not guarantee high-efficiency operation. A photogenerated change in energy that is freely available to do useful work is also essential, and is strongly dependent on semiconductor optical properties and reaction kinetics. Notably, this information reveals that specificity of interfacial chemical reactions – even when they are not preceded by charge separation elsewhere – can result in efficient solar energy conversion. We expect that this tutorial will guide researchers in their pursuit to uncover new mechanisms for light to perform useful work.

14 SOLAR ENERGY↗
DOE OSTI2026

Personal and environmental predictors of polycyclic aromatic hydrocarbon exposure identified through repeated silicone wristband sampling

This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.

Bonner, Emily↗
NASA NTRS1996

Spray Combustion Modeling with VOF and Finite-Rate Chemistry

A spray atomization and combustion model is developed based on the volume-of-fluid (VOF) transport equation with finite-rate chemistry model. The gas-liquid interface mass, momentum and energy conservation laws are modeled by continuum surface force mechanisms. A new solution method is developed such that the present VOF model can be applied for all-speed range flows. The objectives of the present study are: (1) to develop and verify the fractional volume-of-fluid (VOF) cell partitioning approach into a predictor-corrector algorithm to deal with multiphase (gas-liquid) free surface flow problems; (2) to implement the developed unified algorithm in a general purpose computational fluid dynamics (CFD) code, Finite Difference Navier-Stokes (FDNS), with droplet dynamics and finite-rate chemistry models; and (3) to demonstrate the effectiveness of the present approach by simulating benchmark problems of jet breakup/spray atomization and combustion. Modeling multiphase fluid flows poses a significant challenge because a required boundary must be applied to a transient, irregular surface that is discontinuous, and the flow regimes considered can range from incompressible to highspeed compressible flows. The flow-process modeling is further complicated by surface tension, interfacial heat and mass transfer, spray formation and turbulence, and their interactions. The major contribution of the present method is to combine the novel feature of the Volume of Fluid (VOF) method and the Eulerian/Lagrangian method into a unified algorithm for efficient noniterative, time-accurate calculations of multiphase free surface flows valid at all speeds. The proposed method reformulated the VOF equation to strongly couple two distinct phases (liquid and gas), and tracks droplets on a Lagrangian frame when spray model is required, using a unified predictor-corrector technique to account for the non-linear linkages through the convective contributions of VOF. The discontinuities within the sharp interface will be modeled as a volume force to avoid stiffness. Formations of droplets, tracking of droplet dynamics and modeling of the droplet breakup/evaporation, are handled through the same unified predictor-corrector procedure. Thus the new algorithm is non-iterative and is flexible for general geometries with arbitrarily complex topology in free surfaces. The FDNS finite-difference Navier-Stokes code is employed as the baseline of the current development. Benchmark test cases of shear coaxial LOX/H2 liquid jet with atomization/combustion and impinging jet test cases are investigated in the present work. Preliminary data comparisons show good qualitative agreement between data and the present analysis. It is indicative from these results that the present method has great potential to become a general engineering design analysis and diagnostics tool for problems involving spray combustion.

Chen, Yen-Sen↗
DOE OSTI2021

Ab Initio Direct Dynamics

The reactivity and dynamics of molecular systems can be explored computationally by classical trajectory calculations. The traditional approach involves fitting a functional form of a potential energy surface (PES) to the energies from a large number of electronic structure calculations and then integrating numerous trajectories on this fitted PES to model the molecular dynamics. The ever-decreasing cost of computing and continuing advances in computational chemistry software have made it possible to use electronic structure calculations directly in molecular dynamics simulations without first having to construct a fitted PES. In this “on-the-fly” approach, every time the energy and its derivatives are needed for the integration of the equations of motion, they are obtained directly from quantum chemical calculations. This approach started to become practical in the mid-1990s as a result of increased availability of inexpensive computer resources and improved computational chemistry software. The application of direct dynamics calculations has grown rapidly over the last 25 years and would require a lengthy review article. The present Account is limited to some of our contributions to methods development and various applications. To improve the efficiency of direct dynamics calculations, we developed a Hessian-based predictor-corrector algorithm for integrating classical trajectories. Hessian updating made this even more efficient. Furthermore, this approach was also used to improve algorithms for following the steepest descent reaction paths. For larger molecular systems, we developed an extended Lagrangian approach in which the electronic structure is propagated along with the molecular structure. Strong field chemistry is a rapidly growing area, and to improve the accuracy of molecular dynamics in intense laser fields, we included the time-varying electric field in a novel predictor-corrector trajectory integration algorithm. Since intense laser fields can excite and ionize molecules, we extended our studies to include electron dynamics. Specifically, we developed code for time-dependent configuration interaction electron dynamics to simulate strong field ionization by intense laser pulses. Our initial application of ab initio direct dynamics in 1994 was to CH 2 O → H 2 + CO; the calculated vibrational distributions in the products were in very good agreement with experiment. In the intervening years, we have used direct dynamics to explore energy partitioning in various dissociation reactions, unimolecular dissociations yielding three fragments, reactions with branching after the transition state, nonstatistical dynamics of chemically activated molecules, dynamics of molecular fragmentation by intense infrared laser pulses, selective activation of specific dissociation channels by aligned intense infrared laser fields, angular dependence of strong field ionization, and simulation of sequential double ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗
NASA NTRS1992

Bone mineral density, muscle strength, and recreational exercise in men

Muscle strength has been shown to predict bone mineral density (BMD) in women. We examined this relationship in 50 healthy men who ranged in age from 28 to 51 years (average 38.3 years). BMD of the lumbar spine, proximal femur, whole body, and tibia were measured by dual-energy x-ray absorptiometry (Hologic QDR 1000W). Dynamic strength using one repetition maximum was assessed for the biceps, quadriceps, and back extensors and for the hip abductors, adductors, and flexors. Isometric grip strength was measured by dynamometry. Daily walking mileage was assessed by 9 week stepmeter records and kinematic analysis of video filming. Subjects were designated as exercisers and nonexercisers. Exercisers participated in recreational exercise at least two times each week. The results demonstrated that BMD at all sites correlated with back and biceps strength (p < 0.01 to p = 0.0001). Body weight correlated with tibia and whole-body BMD (p < 0.001); age negatively correlated with Ward's triangle BMD (p < 0.01). In stepwise multiple regressions, back strength was the only independent predictor of spine and femoral neck density (R2 = 0.27). Further, back strength was the most robust predictor of BMD at the trochanter, Ward's triangle, whole body, and tibia, although biceps strength, age, body weight, and leg strength contributed significantly to BMD at these skeletal sites, accounting for 35-52% of the variance in BMD. Exercisers and nonexercisers were similar for walking (3.97 versus 3.94 miles/day), age (37.8 versus 38.5) years, and weight (80.0 versus 77.7 kg). However, BMD and muscle strength were significantly greater in exercises than in nonexercisers.(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Center ARC↗
NASA NTRS2016

Risk of Visual Impairment and Intracranial Hypertension After Space Flight: Evaluation of the Role of Polymorphism of Enzymes Involved in One-Carbon Metabolism

Data from the Nutritional Status Assessment protocol provided biochemical evidence that the one-carbon metabolic pathway may be altered in individuals experiencing vision-related issues during and after space flight (1, 2). Briefly, serum concentrations of homocysteine, cystathionine, 2-methylcitric acid, and methylmalonic acid were significantly (P<0.001) higher (25-45%) in astronauts with ophthalmic changes than in those without such changes (1). These differences existed before, during, and after flight. Serum folate was lower (P<0.01) during flight in individuals with ophthalmic changes. Preflight serum concentrations of cystathionine and 2-methylcitric acid, and mean in-flight serum folate, were significantly (P<0.05) correlated with postflight changes in refraction (1). A follow-up study was conducted to evaluate a small number of known polymorphisms of enzymes in the one-carbon pathway, and to evaluate how these relate to vision and other medical aspects of the eye. Specifically, we investigated 5 polymorphisms in MTRR, MTHFR, SHMT, and CBS genes and their association with ophthalmic changes after flight in 49 astronauts. The number of G alleles of MTRR 66 and C alleles of SHMT1 1420 both contributed to the odds of visual disturbances (3). Block regression showed that B-vitamin status at landing and genetics were significant predictors for many of the ophthalmic outcomes studied (3). In conclusion, we document an association between MTRR 66 and SHMT1 1420 polymorphisms and space flightinduced vision changes. These data document that individuals with an altered 1-carbon metabolic pathway may be predisposed to anatomic and/or physiologic changes that render them susceptible to ophthalmic damage during space flight.

Smith, S. M.↗
DOE OSTI2021

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport (EWR)

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular perceived inequity that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based scheduling of internal departures by the Traffic Management Advisor (TMA) (now operationally superseded by the FAA's the Time-Based Flow Management system). Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot easily fit into the arrival flow at the runway. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. TMA scheduling delays are found to be generally higher than TMA airborne metering delays, regardless of prior GDP delays. Depending on how the double delay is defined, between 42 and 62 of all internal departures in GDP and TMA scheduling experienced double delays in this period. A deep dive into the data reveals that contributors to double delays include upstream flights departing before their Expect Departure Clearance Times (EDCTs); differences in the rates used for setting EDCTs and TMA Scheduled Times of Arrival; differences in the arrival demand expected based on EDCTs and the arrival demand entering TMA; and shorter en route times between takeoff and entry into TMA than assumed in the calculation of flight EDCTs, all of which undermine the sequencing and spacing underlying flight EDCTs. Double delays are also found to coincide with periods in which the virtual runway arrival queue being served by a TMA is large, there are periods of high demand relative to capacity, and there are high airborne metering delays. Data mining techniques are used to confirm that each of these factors contribute to the occurrence of double delay andor high internal departure scheduling delay across three months of data from June to August 2010. Predictors of the occurrence of double delay and high TMA scheduling delay are built using logistic regression, providing prediction accuracies of 69 and 73, respectively.

traffic flow management↗
NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗
NASA NTRS2018

Reliability Analysis in the Presence of Aleatory Uncertainty

This paper proposes a method for modeling a system’s response using data. In contrast to approaches that identify a limit state function, we focus on the case in which not all uncertain parameters affecting the response are observable and the measured response is corrupted by noise. To this end, the system response is not characterized by a limit state function but instead by a Random Predictor Model (RPM) having a nonparametric structure. Consequently, the resulting failure probability is not a scalar but a random variable. This variable accounts for the aleatory contributions of the model-form uncertainty and the measurement noise into the response. Furthermore, we propose a framework that enables trading off the predicted range of failure probabilities resulting from such an analysis with a measure of risk. In this context, risk is the percentage of all predicted outcomes the analyst is willing to ignore. The reliability analysis of an aeroelastic structure subject to flutter is used to illustrate the ideas proposed.

Crespo, L. G.↗
NASA NTRS2014

Space Mission Human Reliability Analysis (HRA) Project

The purpose of the Space Mission Human Reliability Analysis (HRA) Project is to extend current ground-based HRA risk prediction techniques to a long-duration, space-based tool. Ground-based HRA methodology has been shown to be a reasonable tool for short-duration space missions, such as Space Shuttle and lunar fly-bys. However, longer-duration deep-space missions, such as asteroid and Mars missions, will require the crew to be in space for as long as 400 to 900 day missions with periods of extended autonomy and self-sufficiency. Current indications show higher risk due to fatigue, physiological effects due to extended low gravity environments, and others, may impact HRA predictions. For this project, Safety & Mission Assurance (S&MA) will work with Human Health & Performance (HH&P) to establish what is currently used to assess human reliabiilty for human space programs, identify human performance factors that may be sensitive to long duration space flight, collect available historical data, and update current tools to account for performance shaping factors believed to be important to such missions. This effort will also contribute data to the Human Performance Data Repository and influence the Space Human Factors Engineering research risks and gaps (part of the HRP Program). An accurate risk predictor mitigates Loss of Crew (LOC) and Loss of Mission (LOM).The end result will be an updated HRA model that can effectively predict risk on long-duration missions.

Boyer, Roger↗
NASA NTRS2001

Ensemble Cannonical Correlation Prediction of Seasonal Precipitation Over the US

This paper presents preliminary results of an ensemble cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.

Lau, William K. M.↗
NASA NTRS2001

Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

Lau, William K. M.↗
DOE OSTI2022

Mineral Protection and Resource Limitations Combine to Explain Profile-Scale Soil Carbon Persistence

The fate of soil carbon (C) is largely controlled by microbial oxidation of organic matter (OM), which is constrained by a variety of mechanisms. OM association with soil minerals provides pronounced protection against microbial decomposition. However, factors such as climate, occlusion, and resource limitations also contribute to OM preservation. Here, we explore the factors explaining C distribution and age within an upland rainforest soil in Hawai'i, a site with abundant preferential flow paths (PFPs) and high short-range order (SRO) mineral content. We characterized lateral and vertical changes in Δ 14 C, SRO mineral content, C-functional group chemistry, and microbial community composition to elucidate the contributions of multiple protection mechanisms to OM preservation. Consistent with our expectation, SRO mineral content and Δ 14 C were strongly correlated (R 2 = 0.95), indicating strong mineral protection of OM throughout the profile. Surprisingly, distance from PFP was also a significant predictor of Δ 14 C and improved model fit, particularly in the shallow horizons (R 2 = 0.97). Elevated C/N ratios, decreased microbial abundance, and greater SRO mineral content suggest nitrogen limitations and enhanced mineral protection constrain OM turnover with distance from PFPs in deep, subsurface mineral horizons. Steady microbial abundance, increasing putative anaerobe abundance, and changes in C-functional group chemistry indicate oxygen limitations constrain OM turnover in the matrix of shallow mineral horizons. Given that oxygen and nutrient limitations contribute to OM preservation in this high SRO system—an exemplar of mineral protection—resource limitations may play an even more important role in OM preservation in other well-structured soils.

59 BASIC BIOLOGICAL SCIENCES↗