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At least 325 records · Page 18
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The INSTEP Monitoring Network: Merging High-and-Low Cost Measurements to Characterize California Wildfires

Despite challenges with data quality and scope, low-cost sensor networks have skyrocketed in popularity over the last 15 years, making air quality data available on refined spatial scales. More recently, studies have leveraged both high and low-quality instruments to create stronger “hybrid” models, with most studies focusing on particulate matter. Low-cost measurements typically represent ground-level emissions only, providing context for human health issues from climate change-driven events such as wildfires. Since low-cost sensors’ capabilities are localized, daily events and microclimates tend to dominate the data rather than larger regional or atmospheric trends. Likewise, their low cost explains their high uncertainty. In contrast, some regulatory-grade instruments produce column measurements as well, providing reliable information on a broader scope. To bridge this gap while expanding into gas-phase measurements, we deployed 12 air quality sensor packages in California, USA during the 2022 wildfire season. These INSTEP (Inexpensive Network Sensor Technology Exploring Pollution) monitors measure carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and several hydrocarbons including methane (CH4) and formaldehyde (HCHO). Half of the monitors were co-located with remote sensing spectrometers: NASA Pandora and Total Column Carbon Observing Network (TCCON). The overlap in pollutants includes NO2, O3, and HCHO between the INSTEP monitors and the Pandora column measurements. TCCON covers column CO, CO2, and CH4, rounding out our comparison. Most of the monitors were distributed throughout the San Francisco Bay area, and an additional three were located within 100 km of Los Angeles. The sites ranged in geographic and population characteristics, including desert, mountainous, coastal, and urban locations. Since varying environmental conditions such as temperature and pressure are known to challenge sensor performance, we will apply newer sensor “calibration” techniques meant to combat this. We will normalize our sensor signals by z-scoring them prior to applying a single calibration model in the form of multivariate linear regression or an artificial neural network. While this technique has been validated for the hydrocarbon and ozone sensor types (metal oxide), it has not yet been tested on electrochemical and non-dispersive infrared sensors, which are also used in the INSTEP monitors. This will serve as a test to see if this normalization technique – or another – is most effective in accounting for environmental differences among sensors. Related data analysis efforts have found success with a variety of geospatial analysis techniques, including weighted network models in which high-quality instruments are given higher weights than their low-cost counterparts. Our preliminary analysis will focus on kriging, which uses a Gaussian algorithm to assign weights, providing estimated pollution levels at locations between monitors. Smoke trajectory and evolution will also be considered using both measurement types. We also aim to baseline subtract our emission estimates from each region to determine which portion of emissions are regional and local, further characterizing burn differences in northern and southern California fires. Future directions include using INSTEP jointly with TEMPO satellite data, and mobile deployments on aircraft and uncrewed aerial vehicles (UAV).

Low-cost sensors↗
NASA NTRS1991

Preliminary determination of Pacific-North America relative motion in the southern Gulf of California using the Global Positioning System

GPS data from experiments conducted in 1985 and 1989 in the southern Gulf of California, Mexico, allow a determination of relative motion between the Pacific and North American plates. The data indicate motion by Cabo San Lucas on the Pacific plate relative to North America at a rate of 47 + or - 7 mm/yr and azimuth of 57 + or - 6 deg west of north, equivalent within uncertainties to the NUVEL-1 global plate motion model.

Dixon, T. H.↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing suburban soil and microbial properties along a soil age chronosequence

Abstract Rapid urbanization is drastically altering ecosystem processes in landscapes around the world. In particular, suburban residential neighborhoods comprise novel ecosystems with water and nutrient inputs that differ greatly from the surrounding land area. These impacts generate concern over the sustainability of urban ecosystems, especially whether they will be characterized by net carbon gain or loss over time. To address this knowledge gap, we established a chronosequence of residential yards in Southern California to test how urban soils change after development. We predicted that urbanized soils would experience shifts in physical characteristics and microbial function over time consistent with ecological succession theory, but residential soils would maintain novel moisture and nutrient regimes compared to undeveloped soils, never “recovering” to a pre‐developed state. We compared different vegetation types to quantify impacts of homeowner landscaping choices and characterized yard soils and their microbial communities. We found that yard soils were nutrient‐ and moisture‐enriched compared to an adjacent undeveloped ecosystem, and turfgrass was associated with higher levels of water and nitrogen. Despite high respiration rates, yard soils accumulated carbon and nitrogen over time. We conclude that suburban residential soils comprise dynamic and heterogeneous ecosystems that are highly influenced by landscaping choices and management practices, and warrant closer study at small management‐relevant scales.

Suratt, Andie [Department of Ecology and Evolution↗
DOE OSTI2024

Long-term drought promotes invasive species by reducing wildfire severity

Anthropogenic climate change has increased the frequency of drought, wildfire, and invasions of non-native species. Although high-severity fires linked to drought can inhibit recovery of native vegetation in forested ecosystems, it remains unclear how drought impacts the recovery of other plant communities following wildfire. We leveraged an existing rainfall manipulation experiment to test the hypothesis that reduced precipitation, fuel load, and fire severity convert plant community composition from native shrubs to invasive grasses in a Southern California coastal sage scrub system. We measured community composition before and after the 2020 Silverado wildfire in plots with three rainfall treatments. Drought reduced fuel load and vegetation cover, which reduced fire severity. Native shrubs had greater prefire cover in added water plots compared to reduced water plots. Native cover was lower and invasive cover was higher in postfire reduced water plots compared to postfire added and ambient water plots. Our results demonstrate the importance of fuel load on fire severity and plant community composition on an ecosystem scale. Management should focus on reducing fire frequency and removing invasive species to maintain the resilience of coastal sage scrub communities facing drought. In these communities, controlled burns are not recommended as they promote invasive plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking

The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. In conclusion, the interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Spatial evidence of cryptic methane cycling and methylotrophic metabolisms along a land–ocean transect in salt marsh sediment

Methylotrophic methanogenesis in the sulfate-rich zone of coastal and marine sediments couples with anaerobic oxidation of methane (AOM), forming the cryptic methane cycle. This study provides evidence of cryptic methane cycling in the sulfate-rich zone across a land–ocean transect of four stations–two brackish, one marine, and one hypersaline–within the Carpinteria Salt Marsh Reserve (CSMR), southern California, USA. Samples from the top 20 cm of sediment from the transect were analyzed through geochemical and molecular (16S rRNA) techniques, in-vitro methanogenesis incubations, and radiotracer incubations utilizing 35 S-SO 4 , 14 C-mono-methylamine, and 14 C-CH 4 . Sediment methane concentrations were consistently low (3 to 28 µM) at all stations, except for the marine station, where methane increased with depth reaching 665 µM. Methanogenesis from mono-methylamine was detected throughout the sediment at all stations with estimated CH 4 production rates in the sub-nanomolar to nanomolar range per cm 3 sediment and day. 16S rRNA analysis identified methanogenic archaea (Methanosarcinaceae, Methanomassiliicoccales, and Methanonatronarchaeacea) capable of producing methane from methylamines in sediment where methylotrophic methanogenesis was found to be active. Metabolomic analysis of porewater showed mono-methylamine was mostly undetectable (<3 µM) or present in trace amounts (<10 µM) suggesting rapid metabolic turnover. In-vitro methanogenesis incubations of natural sediment showed no linear methane buildup, suggesting a process limiting methane emissions. AOM activity, measured with 14 C-CH 4 , overlapped with methanogenesis from mono-methylamine activity at all stations, with rates ranging from 0.03 to 19.4 nmol cm −3 d −1 . Geochemical porewater analysis showed the CSMR sediments are rich in sulfate and iron. Porewater sulfate concentrations (9–91 mM) were non-limiting across the transect, supporting sulfate reduction activity (1.5–2,506 nmol cm −3 d −1 ). Porewater sulfide and iron (II) profiles indicated that the sediment transitioned from a predominantly iron-reducing environment at the two brackish stations to a predominantly sulfate-reducing environment at the marine and hypersaline stations, which coincided with the presence of phyla (Desulfobacterota) involved in these processes. AOM activity overlapped with sulfate reduction and porewater iron (II) concentrations suggesting that AOM is likely coupled to sulfate and possibly iron reduction at all stations. However, 16S rRNA analysis identified anaerobic methanotrophs (ANME-2) only at the marine and hypersaline stations while putative methanogens were found in sediment across all stations. In one sediment horizon at the marine station, methanogen families (Methanosarcinaceae, Methanosaetaceae, Methanomassiliicoccales, and Methanoregulaceae) and ANME 2a,2b, and 2c groups were found together. Collectively, our data suggest that at the brackish stations methanogens alone may be involved in cryptic methane cycling, while at the marine and hypersaline stations both groups may be involved in the process. Differences in rate constants from incubations with 14 C-labeled methane and mono-methylamine suggest a non-methanogenic process oxidizing mono-methylamine to inorganic carbon, likely mediated by sulfate-reducing bacteria. Understanding the potential competition of sulfate reducers with methanogens for mono-methylamine needs further investigation as it might be another important process responsible for low methane emissions in salt marshes.

Anaerobic oxidation of methane↗
DOE OSTI2021

The influence of preexisting host rock damage on fault network localization

The transition from stable to unstable fracture propagation occurs when fractures begin to interact and link. Thus, fracture network coalescence controls how rocks and engineered structures fail. Here, to constrain the factors that influence localization in shear zones under brittle conditions, we build discrete element method models with a rough fault embedded in a shear zone. We add varying numbers of diffuse, randomly-placed weaknesses to examine the influence of diffuse damage on fracture network localization. The number of weaknesses controls the localization behavior of the fault network and the final fault geometry. We quantify localization using the Gini coefficient of the fracture volume, which measures the nonuniformity in a population. Each model generally increases in localization toward failure. However, models with more diffuse damage experience delocalization phases that are superimposed on the overall trend of increasing localization. The observed link between delocalization and host rock damage may help explain the varying localization of low magnitude seismicity in southern California. Models with more diffuse damage produce more complex fault geometries comprised of several parallel strands of wing cracks. The propagation of these wing cracks reduces the shear stress acting on the model boundaries, indicating that this fracture development increases the mechanical efficiency of the system.

58 GEOSCIENCES↗
DOE OSTI2025

An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment

Cataloging and classifying trees in the urban environment is a crucial step in urban and environmental planning; however, manual collection and maintenance of this data is expensive and time-consuming. Although algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, they generally struggle in the more varied urban environment. This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach. Our results highlight the importance of fusing spectral and structural information for remote sensing tasks in complex urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗
DOE OSTI2020

Detection Limits and Near‐Field Ground Motions of Fast and Slow Earthquakes

Abstract We investigate theoretical limits to detection of fast and slow seismic events, and spatial variations of ground motion expected from M 6 earthquakes at short epicentral distances. The analyses are based on synthetic velocity seismograms calculated with the discrete wavenumber method assuming seismic velocities and attenuation properties of the crust in Southern California. The examined source properties include different magnitudes ( M −1.0 to M 6.0), static stress drops (0.1–10 MPa), and slow and fast ruptures (0.1–0.9 of shear wave velocity). For the M 6 events we also consider variations in rise times producing crack‐ and pulse‐type events and different rupture directivities. Slow events produce ground motion with considerably lower amplitude than corresponding regular fast earthquakes with the same magnitude, and hence are significantly more difficult to detect. The static stress drop and slip rise time also affect the maximum radiated seismic motion, and hence event detectability. Apart from geometrical factors, the saturation and depletion of seismic ground motion at short epicentral distances stem from radiation pattern, earthquake size (magnitude, stress drop), and rupture directivity. The rupture velocity, rise time, and directivity affect significantly the spatial pattern of the ground motions. The results can help optimizing detection of slow and fast small earthquakes and understand the spatial distribution of ground motion generated by large events.

58 GEOSCIENCES↗
DOE OSTI2020

Analysis of Fault Zone Resonance Modes Recorded by a Dense Seismic Array Across the San Jacinto Fault Zone at Blackburn Saddle

We present observations and modeling of spatial eigen-functions of resonating waves within fault zone waveguide, using data recorded on a dense seismic array across the San Jacinto Fault Zone (SJFZ) in southern California. The array consists of 5-Hz geophones that cross the SJFZ with ~10-30 m spacing at the Blackburn Saddle near the Hemet Stepover. Wavefield snapshots after the S wave arrival are consistent for more than 50 near-fault events, suggesting that this pattern is controlled by the fault zone structure rather than source properties. Data from example event with high signal to noise ratio shows three main frequency peaks at ~1.3 Hz, ~2.0 Hz, and ~2.8 Hz in the amplitude spectra of resonance waves averaged over stations near the fault. The data are modeled with analytical expressions for eigen-functions of resonance waves in a low-velocity layer (fault zone) between two quarter-spaces. Using a grid search-based method, we investigate the possible width of the waveguide, location within the array, and shear wave velocities of the media that fit well the resonance signal at ~1.3 Hz. The results indicate a ~300 m wide damaged fault zone layer with ~65% S wave velocity reduction compared to the host rock. The SW edge of the low-velocity zone is near the mapped fault surface trace, indicating that the damage zone is asymmetrically located at the regionally faster NE crustal block. The imaging resolution of the fault zone structure can be improved by modeling fault zone resonance modes and trapped waves together.

58 GEOSCIENCES↗
DOE OSTI2021

Data–Driven Velocity Model Evaluation Using K–Means Clustering

In this work, we develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.

58 GEOSCIENCES↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2024

Enhancing Regional Seismic Velocity Models With Higher‐Resolution Local Results Using Sparse Dictionary Learning

Abstract We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with data in the common region of both models, the method can enhance a regional lower‐resolution model to match the style and resolution of local higher‐resolution results while preserving its regional coverage. The method is demonstrated by applying it to two‐dimensional V S and three‐dimensional V P and V S regional and local velocity models in southern California. The enhanced reconstructed regional results exhibit clear visual improvements, especially in the reconstructed V P / V S ratios, and better correlations with geological features. Moreover, the reconstructed regional V P , V S models outperform the original ones in comparison of simulated earthquake waveforms to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications of data enhancement in earth sciences.

Geochemistry & Geophysics↗
DOE OSTI2025

Fault Network Geometry Modulates Earthquake Source Spectra Across Scales

Earthquake source spectra provide unique insights into the earthquake rupture process. Motivated by previous research suggesting that complex fault geometries enhance high‐frequency seismic radiation, we study the influence of fault network geometry on earthquake source spectra using multiple independent observations. At regional scales, we examine correlations of stress drop measurements with surface fault trace misalignment in Southern California, Japan, and Central Italy. At a global scale, we examine correlations of moment‐rate function complexity of large earthquakes with focal mechanism variability, a proxy for local fault complexity. Despite significant scatter in the observations, we find overall consistent positive correlations. The concept that elastic interactions of discrete fault structures during the earthquake rupture process generates high‐frequency ground motions offers a coherent framework for interpreting our observations. These findings suggest that variations in fault complexity explain why some earthquakes produce stronger high‐frequency ground motions than others.

Lee, Jaeseok [Brown Univ., Providence, RI (United ↗
DOE OSTI2025

Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective

We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.

information theory↗
DOE OSTI2025

Climate-driven succession in marine microbiome biodiversity and biogeochemical function

Abstract Seasonal and El Niño-Southern Oscillation (ENSO) warming result in similar ocean changes as predicted with climate change. Climate-driven environmental cycles have strong impacts on microbiome diversity, but impacts on microbiome function are poorly understood. Here we quantify changes in microbial genomic diversity and functioning over 11 years covering seasonal and ENSO cycles at a coastal site in the southern California Current. We observe seasonal oscillations between large-genome lineages during cold, nutrient rich conditions in winter and spring versus small-genome lineages, including Prochlorococcus and Pelagibacter , in summer and fall. Parallel interannual changes separate communities depending on ENSO condition. Biodiversity shifts translate into clear oscillations in microbiome functional potential. Ocean warming induced an ecosystem with less iron but more macronutrient stress genes, depressed organic carbon degradation potential and biomass, and elevated carbon-to-nutrient biomass ratios. The consistent microbial response observed across time-scales points towards large climate-driven changes in marine ecosystems and biogeochemical cycles.

Larkin, Alyse A. (ORCID:0000000344660791)↗