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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The NEON Daily Isotopic Composition of Environmental Exchanges Dataset

The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ 2 H, δ 18 O) and carbon dioxide (δ 13 C) at different tower heights, as well as aggregated biweekly precipitation samples (δ 2 H, δ 18 O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ 2 H, δ 18 O), evapotranspiration (ET; δ 2 H, δ 18 O), and net ecosystem exchange (NEE; δ 13 C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ 2 H, δ 18 O, and δ 13 C flux data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Community Structure and Microbial Associations in Sediment-Free Methanotrophic Enrichment Cultures from a Marine Methane Seep

We report Syntrophic consortia of anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB) consume large amounts of methane and serve as the foundational microorganisms in marine methane seeps. Despite their importance in the carbon cycle, research on the physiology of ANME-SRB consortia has been hampered by the slow growth and complex physicochemical environment the consortia inhabit. Here, we report successful sediment-free enrichment of ANME-SRB consortia from deep-sea methane seep sediments in the Santa Monica Basin, California. Anoxic Percoll density gradients and size-selective filtration were used to separate ANME-SRB consortia from sediment particles and single cells to accelerate the cultivation process. Over a 3-year period, a subset of the sediment-associated ANME and SRB lineages, predominantly comprised of ANME-2a/2b (“Candidatus Methanocomedenaceae”) and their syntrophic bacterial partners, SEEP-SRB1/2, adapted and grew under defined laboratory conditions. Metagenome-assembled genomes from several enrichments revealed that ANME-2a, SEEP-SRB1, and Methanococcoides in different enrichments from the same inoculum represented distinct species, whereas other coenriched microorganisms were closely related at the species level. This suggests that ANME, SRB, and Methanococcoides are more genetically diverse than other members in methane seeps. Flow cytometry sorting and sequencing of cell aggregates revealed that Methanococcoides, Anaerolineales, and SEEP-SRB1 were overrepresented in multiple ANME-2a cell aggregates relative to the bulk metagenomes, suggesting they were physically associated and possibly interacting. Overall, this study represents a successful case of selective cultivation of anaerobic slow-growing microorganisms from sediments based on their physical characteristics, introducing new opportunities for detailed genomic, physiological, biochemical, and ecological analyses. Biological anaerobic oxidation of methane (AOM) coupled with sulfate reduction represents a large methane sink in global ocean sediments. Methane consumption is carried out by syntrophic archaeal-bacterial consortia and fuels a unique ecosystem, yet the interactions in these slow-growing syntrophic consortia and with other associated community members remain poorly understood. The significance of this study is the establishment of sediment-free enrichment cultures of anaerobic methanotrophic archaea and sulfate-reducing bacteria performing AOM with sulfate using selective cultivation approaches based on size, density, and metabolism. By reconstructing microbial genomes and analyzing community composition of the enrichment cultures and cell aggregates, we shed light on the diversity of microorganisms physically associated with AOM consortia beyond the core syntrophic partners. These enrichment cultures offer simplified model systems to extend our understanding of the diversity of microbial interactions within marine methane seeps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

N 2 Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions

Nitrogen (N) fertilizer supports global food production, but its use and overuse drive emissions of nitrous oxide (N 2 O), a potent and long-lived greenhouse gas. Understanding the drivers of N 2 O fluxes remains elusive, making it difficult to predict emissions in time and space and to develop and evaluate ways to lower emissions through management. Major scientific uncertainties underlying the understanding of the drivers of N 2 O fluxes identified in a workshop of N 2 O emissions experts include poor process-based understanding of controls on soil N 2 O emissions in the field; insufficient data to reduce uncertainty in N 2 O budgets from the field to regional scales, including N 2 O emission measurements and importantly, field-scale N balances; and high uncertainty in model predictions of soil N 2 O emissions across environmental and management conditions. To reduce these uncertainties, we present the concept of N 2 Onet, a global collaborative initiative to accelerate advances in N 2 O measurement, analyses, and mitigation. N 2 Onet will serve as an observational network of supersites with multi-scale measurements; a database hub for N 2 O flux and ancillary data; and a catalyst for community building, information sharing, and training. By coalescing and coordinating the global community of researchers, N 2 Onet will provide a roadmap for reducing N 2 O emissions from agriculture worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Prescribed fires, smoke exposure, and hospital utilization among heart failure patients

Abstract Background Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients’ hospital utilization. Methods We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients’ primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI). Results Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies. Conclusions Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires.

Raab, Henry↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2021

Time-lapse approach to correct deficiencies of 2D soil zymography

Membrane zymography is commonly used in rhizosphere ecology to map enzyme activities in intact soil samples and plant roots. The method consists of incubating a membrane saturated with an enzyme-specific fluorogenic substrate on the soil/root surface followed by measurements of fluorescence intensity of the product in the membrane. The traditional zymography is based on assumptions that fluorescence on membrane images is linearly increasing with time during zymography and an increase in product content is numerically equal to enzyme activity in soil below the membrane. These assumptions are unlikely to hold in experimental settings. In this work we introduce a new zymography technique, time-lapse zymography (TLZ); the approach that eliminates the need for assumptions of the traditional zymography and provides more realistic estimates of enzymatic activities. We assessed the new technique in a series of laboratory and modeling experiments, including quantification of the fluorescent product diffusion (e.g. MUF: 4-methylumbelliferone) from plant roots, enzyme activity measurements on the roots, and HYDRUS-2D & HP2 software calibration with obtained data. The calibrated model was used to analyze the processes governing spatial and temporal dynamics of MUF contents in the membrane. The results indicated that the enzyme diffusion within the membrane-soil system was negligible, and measured zymograms were adequately reproduced solely by accounting for substrate and product diffusions and for catalytic enzyme reaction described by the Michaelis-Menten equation. TLZ enabled identifying and using linear parts on MUF time series and accounting for MUF losses in each zymogram pixel, considerably improving the accuracy as compared to the traditional method. Results demonstrated that enzymatic activity from only a thin soil layer (~0.2 mm) is adequately represented in zymograms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Abstract Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development.

Environmental Sciences & Ecology↗
DOE OSTI2022

The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information

Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally

ABSTRACT Forest allocation of net primary productivity (NPP) to reproduction (carbon required for flowers, fruits, and seeds) is poorly quantified globally, despite its critical role in forest regeneration and a well‐supported trade‐off with allocation to growth. Here, we present the first global synthesis of a biometric proxy for forest reproductive allocation (RA) across environmental and stand age gradients from a compiled dataset of 824 observations across 393 sites. We find that ecosystem‐scale RA increases ~60% from boreal to tropical forests. Climate shows important non‐linear relationships with RA, but is not the sole predictor. Forest age effects are comparable to climate in magnitude (MAT: ß = 0.24,p = 0.021; old growth forest: ß = 0.22,p < 0.001), while metrics of soil fertility show small but significant relationships with RA (soil pH: ß = 0.07,p = 0.001; soil N: ß = −0.07,p = 0.001). These results provide strong evidence that ecosystem‐scale RA is mediated by climate, forest age, and soil conditions, and is not a globally fixed fraction of positive NPP as assumed by most vegetation and ecosystem models. Our dataset and findings can be used by modellers to improve predictions of forest regeneration and carbon cycling.

Environmental Sciences & Ecology↗
DOE OSTI2025

Testing mechanisms of how mycorrhizal associations affect forest soil carbon and nitrogen cycling (Final Technical Report)

Trees are in a symbiotic partnership with mycorrhizal fungi in which they provide the fungi with carbon from photosynthesis and the fungi provide the trees with nutrients and water. In temperate forests, the vast majority of trees form symbioses with one of two types of mycorrhizal fungi—arbuscular mycorrhizal (AM) fungi or ectomycorrhizal (EcM) fungi. These fungi differ in their morphology, hyphal length, and nutrient acquisition strategies. Many studies have found systematic differences in soil organic matter and nitrogen availability between forest stands dominated by AM-associating trees versus EcM-associating trees. For instance, there is a larger proportion of organic matter that is mineral-associated, more available nitrogen, and lower soil carbon to nitrogen ratios in AM forest stands relative to EcM forests stands. However, the mechanisms driving these patterns are not known, which complicates our ability to model soil organic matter dynamics in forested ecosystems. The main objective of this research was to understand the degree to which the observed differences in soil C and N dynamics between AM and EcM dominated forests are driven by tree traits like litter decomposability and root exudation versus mycorrhizal fungal nutrient acquisition strategies. We investigated these mechanisms using observations and targeted experiments and incorporated this knowledge into a process-based soil organic matter model. The observational studies compared the importance of leaf litter decomposability versus fungal identity on soil organic matter processes. We found that often fungal identity and traits were more important drivers of soil organic matter patterns than leaf litter decomposability. We ran two novel experiments: 1) a growth chamber experiment across four EcM and four AM tree species using a 13 C-labeled atmosphere to trace seedling-derived C into hyphae, the rhizosphere, and soil; and 2) an in situ decomposition experiment of six different 13 C and 15 N labeled litters that ranged in decomposability incubated across a gradient of EcM dominance at three sites that capture important variation in climate, soils, and forest species composition. In the first experiment, we found no significant differences of seedling mycorrhizal association on soil carbon sequestration over a growing season, but we did find that mycorrhizal association affected rhizodeposition with EcM-associating seedlings depositing more carbon in response to increased nitrogen availability. The decomposition experiment is still ongoing, but thus far, we have found slower litter decomposition in only one of three EcM-dominated forests which suggests that differences between AM- and EcM-dominated forests depend on the environmental context and identity of the EcM fungi. Lastly, we explicitly incorporated mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model creating Myco-CORPSE. By including the different nutrient acquisition strategies of AM and EcM fungi, we explored the conditions under which EcM fungi can slow decomposition rates and lead to greater soil organic carbon accumulation compared to AM fungi. We found that the effect of EcM fungi was highly context dependent and that EcM fungi decreased decomposition in colder forests with recalcitrant litter inputs and when they produced oxidases and necromass-degrading enzymes. Our research highlights the importance of fungal nutrient acquisition in driving soil organic matter patterns and the need to move beyond the AM-EcM dichotomy to consider the identity and traits of the specific fungi participating in the symbiosis. Overall, this research has resulted in six, peer-reviewed published papers in journals such as Global Change Biology, Ecology (2), Soil Biology and Biochemistry, and Ecosystems (2). There are at least two more papers in progress on this research including one that was recently submitted to Global Change Biology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hydraulic redistribution supplies a major water subsidy and improves water status of understory species in a longleaf pine ecosystem

Hydraulic redistribution (HR) is a common phenomenon in water-limited ecosystems; however, it remains unclear how the volume of water transported via HR compares to other components of the hydrologic budget and how HR influences water availability for understory plant communities. In this study, we investigate the absolute and relative magnitude of HR on a forest water budget and identify potential impacts of this water subsidy to understory plant communities. We scaled tree-level estimates of transpiration and HR of three common tree species naturally occurring in a longleaf pine woodland with plot-level measurements of basal area to determine their magnitude at the stand scale. We trenched plots containing understory vegetation but devoid of mature trees and their connected roots to exclude HR subsidies to understory plant species. We analysed soil water isotopes and assessed leaf water potential (Ψ L ) in trenched and control plots to determine if HR results in mixing of water among soil strata and improves understory plant moisture status. Water inputs from HR were equivalent to >30% of total rainfall for the site during the observation period and ~40% of total tree water uptake, depending on species. A stable isotope mixing model confirmed that soil water within HR-exposed plots was more similar to groundwater, whereas soil water within trenched plots was more similar to precipitation. Exclusion of HR via trenching decreased soil moisture and pre-dawn Ψ L for all understory species. These three lines of evidence suggest that HR from overstory trees redistributes a sizable portion of water from deeper to shallower soil profiles and that this water subsidy enhances understory plant water status.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Abstract Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An HMM approach expands the landscape of sesquiterpene cyclases across the kingdom Fungi

Sesquiterpene cyclases (STC) catalyse the cyclization of the C15 molecule farnesyl diphosphate into a vast variety of mono- or polycyclic hydrocarbons and, for a few enzymes, oxygenated structures, with diverse stereogenic centres. The huge diversity in sesquiterpene skeleton structures in nature is primarily the result of the type of cyclization driven by the STC. Despite the phenomenal impact of fungal sesquiterpenes on the ecology of fungi and their potentials for applications, the fungal sesquiterpenome is largely untapped. The identification of fungal STC is generally based on protein sequence similarity with characterized enzymes. This approach has improved our knowledge on STC in a few fungal species, but it has limited success for the discovery of distant sequences. Besides, the tools based on secondary metabolite biosynthesis gene clusters have shown poor performance for terpene cyclases. Here, we used four sets of sequences of fungal STC that catalyse four types of cyclization, and specific amino acid motives to identify phylogenetically related sequences in the genomes of basidiomycetes fungi from the order Polyporales. We validated that four STC genes newly identified from the genome sequence of Leiotrametes menziesii, each classified in a different phylogenetic clade, catalysed a predicted cyclization of farnesyl diphosphate. We built HMM models and searched STC genes in 656 fungal genomes genomes. We identified 5605 STC genes, which were classified in one of the four clades and had a predicted cyclization mechanism. We noticed that the HMM models were more accurate for the prediction of the type of cyclization catalysed by basidiomycete STC than for ascomycete STC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A ‘Nuclear Bomb’ or Just ‘a Joke’? Groundwater Models May Help Communicate Nuanced Risks to the Great Salt Lake

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed.

Environmental Sciences & Ecology↗
DOE OSTI2023

Automated Symbolic Upscaling: 1. Model Generation for Extended Applicability Regimes

Abstract In porous media theory, upscaling techniques are fundamental to deriving rigorous Darcy‐scale models for flow and reactive transport in subsurface systems. Due to limitations in classical techniques, a number of ad hoc approaches have been proposed to address physical regimes in which reactive time scales are similar to, or faster than, diffusive time scales. In Part 1 of this two part series, we present a strategy for expanding the applicability of classical homogenization theory by generalizing the assumed closure form. We detail the implementation of this strategy on two reactive mass transport problems with moderately reactive physics. The strategy produces nontrivial homogenized models with emergent terms and effective parameters that couple reactive, diffusive, and advective transport. The differences in equation forms between the macroscopic and pore‐scale descriptions advise caution to further studies where the forms of macroscopic equations are assumed, as opposed to rigorously derived. Numerical validation is provided for each problem to show that the error estimates of homogenization theory are satisfied, and to justify the implemented strategy. In Part 2, the presented strategy is automated using symbolic computing to expedite its implementation.

Pietrzyk, Kyle↗
DOE OSTI2023

Marine phytoplankton downregulate core photosynthesis and carbon storage genes upon rapid mixed layer shallowing

Marine phytoplankton are a diverse group of photoautotrophic organisms and key mediators in the global carbon cycle. Phytoplankton physiology and biomass accumulation are closely tied to mixed layer depth, but the intracellular metabolic pathways activated in response to changes in mixed layer depth remain less explored. Here, metatranscriptomics was used to characterize the phytoplankton community response to a mixed layer shallowing (from 233 to 5 m) over the course of two days during the late spring in the Northwest Atlantic. Most phytoplankton genera downregulated core photosynthesis, carbon storage, and carbon fixation genes as the system transitioned from a deep to a shallow mixed layer and shifted towards catabolism of stored carbon supportive of rapid cell growth. In contrast, phytoplankton genera exhibited divergent transcriptional patterns for photosystem light harvesting complex genes during this transition. Active virus infection, taken as the ratio of virus to host transcripts, increased in the Bacillariophyta (diatom) phylum and decreased in the Chlorophyta (green algae) phylum upon mixed layer shallowing. A conceptual model is proposed to provide ecophysiological context for our findings, in which integrated light limitation and lower division rates during transient deep mixing are hypothesized to disrupt resource-driven, oscillating transcript levels related to photosynthesis, carbon fixation, and carbon storage. Our findings highlight shared and unique transcriptional response strategies within phytoplankton communities acclimating to the dynamic light environment associated with transient deep mixing and shallowing events during the annual North Atlantic bloom.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Water isotopes, climate variability, and the hydrological cycle: recent advances and new frontiers

Abstract The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a ‘common currency’ that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

Dee, Sylvia (ORCID:000000022140785X)↗