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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Linking Stream Chemistry to Subsurface Redox Architecture

As drinking-water scarcity grows worldwide, we need to improve predictions of the quantity and quality of our water resources. An overarching problem for model improvement is that we do not know the geological structure of aquifers in sufficient detail. In this work, we demonstrate that mineral-water reactions imprint structure in the subsurface that impacts the flow and transport of some chemical species. Specifically, pyrite, a ubiquitous mineral, commonly oxidizes and depletes in the upper layers of the weathering profile in most humid watersheds, only remaining at depths of meters. We hypothesize that variations in concentrations (C) of pyrite-derived sulfate released into rivers as a function of discharge (q) reflect the rate-limiting step and depth of pyrite-oxidizing layers. We found that log C - log q behaviors thus differ in small and large watersheds in the Susquehanna River Basin as well as in selected watersheds in the Western United States. Although coal mining changes pyrite oxidation from closed to open system with respect to O 2 , patterns in stream chemistry as a function of discharge are consistent with deep and shallow pyrite oxidation zones in small and large watersheds respectively. Therefore, understanding the subsurface patterns of mineral reactions and how they affect the architecture of aquifers will elucidate patterns of changing river chemistry and our ability to manage water resources in the future under accelerated land use and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hydrodynamic Modeling of Stratification and Mixing in a Shallow, Tropical Floodplain Lake

Abstract Floodplain lakes are widespread and ecologically important throughout tropical river systems, however data are rare that describe how temporal variations in hydrological, meteorological and optical conditions moderate stratification and mixing in these shallow lakes. Using time series measurements of meteorology and water‐column temperatures from 17 several day campaigns spanning two hydrological years in a representative Amazon floodplain lake, we calculated surface energy fluxes and thermal stratification, and applied and evaluated a 3‐dimensional hydrodynamic model. The model successfully simulated diel cycles in thermal structure characterized by buoyancy frequency, depth of the actively mixing layer, and other terms associated with the surface energy budget. Diurnal heating with strong stratification and nocturnal mixing were common; despite considerable heat loss at night, the strong stratification during the day meant that mixing only infrequently extended to the bottom at night. Simulations indicated that the diurnal thermocline up and downwelled creating lake‐wide differences in near‐surface temperatures and mixing depths. Infrequent full mixing creates conditions conducive to anoxia in these shallow lakes given their warm temperatures.

Environmental Sciences & Ecology↗
DOE OSTI2024

The Importance of Accounting for Landscape Position When Investigating Grasslands: A Multidisciplinary Characterisation of a California Coastal Grassland

Abstract Grasslands are one of the most common land‐cover types, providing important ecosystem services globally, yet few studies have examined grassland critical‐zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely‐sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up‐, mid‐, and down‐slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co‐varied with soil CO 2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO 2 fluxes were inhibited by high moisture content in the down‐slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post‐rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid‐slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical‐zone environments on hillslopes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Linking Dissolved Organic Matter Composition to Landscape Properties in Wetlands Across the United States of America

Abstract Wetlands are integral to the global carbon cycle, serving as both a source and a sink for organic carbon. Their potential for carbon storage will likely change in the coming decades in response to higher temperatures and variable precipitation patterns. We characterized the dissolved organic carbon (DOC) and dissolved organic matter (DOM) composition from 12 different wetland sites across the USA spanning gradients in climate, landcover, sampling depth, and hydroperiod for comparison to DOM in other inland waters. Using absorption spectroscopy, parallel factor analysis modeling, and ultra‐high resolution mass spectroscopy, we identified differences in DOM sourcing and processing by geographic site. Wetland DOM composition was driven primarily by differences in landcover where forested sites contained greater aromatic and oxygenated DOM content compared to grassland/herbaceous sites which were more aliphatic and enriched in N and S molecular formulae. Furthermore, surface and porewater DOM was also influenced by properties such as soil type, organic matter content, and precipitation. Surface water DOM was relatively enriched in oxygenated higher molecular weight formulae representing HUP High O/C compounds than porewaters, whose DOM composition suggests abiotic sulfurization from dissolved inorganic sulfide. Finally, we identified a group of persistent molecular formulae (3,489) present across all sites and sampling depths (i.e., the signature of wetland DOM) that are likely important for riverine‐to‐coastal DOM transport. As anthropogenic disturbances continue to impact temperate wetlands, this study highlights drivers of DOM composition fundamental for understanding how wetland organic carbon will change, and thus its role in biogeochemical cycling.

Environmental Sciences & Ecology↗
DOE OSTI2024

Fast Seismic Anomalies Under Continents Explained by the Delaminated Lower Continental Crust—Implications From High Pressure‐Temperature Elasticity of Jadeite

Seismic tomography has shown that the shear wave velocities (Vs) under continents, especially under cratons, are extremely fast at 100–200 km depth, which is difficult to explain by low temperatures or high Mg#. Alternatively, delaminated eclogitic lower continental crust has been proposed to account for these fast seismic anomalies. However, the thermoelastic properties of jadeite which constitutes up to 60–80 mol% of clinopyroxene in the potentially delaminated lower continental crust are not well constrained. In this study, we measured the single-crystal elasticity of jadeite by Brillouin spectroscopy under simultaneous high pressure and temperature conditions for the first time. We found that the temperature dependence of Vs of jadeite is extremely small if not negligible. The seismic velocities of the potentially delaminated lower continental crusts were subsequently modeled and found to match the widely observed fast seismic anomalies under cratons between 100 and 200 km depth.

Geology↗
DOE OSTI2024

Seasonal modulation of crustal seismicity in northeastern Japan driven by snow load

Numerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface-load-induced stress perturbation relative to the long-term-averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluating Cloud Feedback Components in Observations and Their Representation in Climate Models

This study quantifies the contribution of individual cloud feedbacks to the total short-term cloud feedback in satellite observations over the period 2002–2014 and evaluates how they are represented in climate models. The observed positive total cloud feedback is primarily due to positive high-cloud altitude, extratropical high- and low-cloud optical depth, and land cloud amount feedbacks partially offset by negative tropical marine low-cloud feedback. Seventeen models from the Atmosphere Model Intercomparison Project of the sixth Coupled Model Intercomparison Project are analyzed. The models generally reproduce the observed moderate positive short-term cloud feedback. However, compared to satellite estimates, the models are systematically high-biased in tropical marine low-cloud and land cloud amount feedbacks and systematically low-biased in high-cloud altitude and extratropical high- and low-cloud optical depth feedbacks. Errors in modeled short-term cloud feedback components identified in this analysis highlight the need for improvements in model simulations of the response of high clouds and tropical marine low clouds. Our results suggest that skill in simulating interannual cloud feedback components may not indicate skill in simulating long-term cloud feedback components.

58 GEOSCIENCES↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toward a Universal Model of Hyporheic Exchange and Nutrient Cycling in Streams

In this paper we demonstrate that several ubiquitous hyporheic exchange mechanisms can be represented simply as a one-dimensional diffusion process, where the diffusivity decays exponentially with depth into the streambed. Based on a meta-analysis of 106 previously published laboratory measurements of hyporheic exchange (capturing a range of bed morphologies, hydraulic conditions, streambed properties, and experimental approaches) we find that the reference diffusivity and mixing length-scale are functions of the permeability Reynolds Number and Schmidt Number. These dimensionless numbers, in turn, can be estimated for a particular stream from the median grain size of the streambed and the stream's depth, slope, and temperature. Application of these results to a seminal study of nitrate removal in 72 headwater streams across the United States, reveals: (a) streams draining urban and agricultural landscapes have a diminished capacity for in-stream and in-bed mixing along with smaller subsurface storage zones compared to streams draining reference landscapes; (b) under steady-state conditions nitrate uptake in the streambed is primarily biologically controlled; and (c) median reaction timescales for nitrate removal in the hyporheic zone are 0.5 and 20 hr for uptake by assimilation and denitrification, respectively. While further research is needed, the simplicity and extensibility of the framework described here should facilitate cross-disciplinary discussions and inform reach-scale studies of pollutant fate and transport and their scale-up to watersheds and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the Relationship Between Methane Production in Anaerobic Incubations of Peat Material and In Situ Methane Emissions

Anaerobic incubations of peat have been widely used to explore soil processes, but this in vitro technique raises many questions as to how well it reproduces in situ conditions. To investigate this, we conducted 60–100 days (+25 days pre‐incubation) anaerobic, temperature‐controlled incubation experiments across a temperature range of 1–26°C on samples from bog and fen habitats, at two different depths (9–19 and 25–35 cm). Here, we observed exponential increases in CO 2 and methane production with temperature in all conditions. We then compared field‐based measurements of methane emission with modeled expectations by extrapolating incubation‐determined methane production rates based on (a) soil temperature profiles, (b) the observed incubation temperature‐methane production relationship, and (c) seasonal thaw depth from each site. The resulting incubation‐extrapolated methane production agreed with measured emission rates within a factor of two at both sites and corresponded to 182 ± 54% and 59 ± 14% of the measured average yearly fluxes from the field for the bog and fen, respectively. The underestimation of fen methane fluxes may be due to the lack of living plant root‐derived dissolved organic carbon inputs in incubations, a key process in fens. Conversely, the overestimation in bogs could be attributed to methane oxidation in the field, which is absent in anaerobic incubation conditions. Nonetheless incubations predicted greenhouse gas emissions from a northern peatland within a factor of two.

Biological and medical sciences,Geosciences,Enviro↗
DOE OSTI2024

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ELM–Wet: Inclusion of a Wet–Landunit With Sub–Grid Representation of Eco–Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter-daily CO 2 and CH 4 flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Subglacial Discharge Effects on Antarctic Ice‐Shelf Basal Melt and the Southern Ocean in a Global, Coupled Ocean—Sea‐Ice Model

Subglacial freshwater from beneath Antarctica enters the ocean at depth, enhancing ice-shelf melting and affecting Southern Ocean properties. To study these effects in an Antarctic-wide context, we use a continental-scale subglacial hydrology model that calculates grounding line freshwater flux for a global, coupled ocean—sea-ice model. We find that subglacial discharge impacts melt rates primarily through continental shelf temperature modification, contrasting with findings from regional studies that do not permit large-scale adjustments. The consequence is that Antarctic melt rates scale with subglacial discharge more strongly than inferred from regional studies. We also find that the addition of buoyancy at depth facilitates heat upwelling to the surface, resulting in higher sea ice volume downstream of cold ice shelves and lower sea ice volume downstream of warm ice shelves. This highlights the drawbacks of simplifications in previous global studies that deposit Antarctic meltwater at the ocean surface and find uniform ocean surface cooling and sea-ice growth. While the patterns we find are robust, we conclude that the addition of subglacial discharge at present-day rates has a small effect on basal melt rates, hydrography, and sea ice. However, stronger discharge can have significant effects and can even accelerate a shift from low to high melting for ice shelves close to such a tipping point. Finally, the importance of feedbacks between enhanced cavity overturning and continental shelf conditions poses a complication for parameterizing subglacial discharge effects on melting for ice-sheet projections that do not include a coupled ocean component.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimating Soil Thermal Inertia Profiles From the Passive Equilibration of a Temperature Probe

Knowledge of the distribution of soil thermal properties is important for understanding subsurface hydrological and biogeochemical processes. This study describes and evaluates quick thermal profiling (QTP), a new measurement technique aimed at providing rapid, depth-resolved measurements of soil thermal inertia at numerous locations across the landscape. A cylindrical probe with temperature sensors at multiple depths is quickly inserted into the ground, and soil thermal inertia is estimated from how quickly the probe temperature equilibrates with the soil. To this end, a finite volume heat transfer model is used to generate temperature equilibration time series across combinations of controlling factors, and a gridded search inversion approach is applied to infer soil thermal inertia. Field tests in the Arctic indicate that QTP measurements have a minimum uncertainty of 0.14 J m −2 K −1 s −1/2 and covary with dual-probe heat pulse thermal analyzer measurements (concordance correlation coefficient = 0.56) with a root-mean-square error of 0.40 J m −2 K −1 s −1/2 . Besides demonstrating the value of QTP for estimating thermal inertia, this study identifies various sources of measurement uncertainty, particularly probe-soil contact resistance and frictional heating. Further, analysis of soil samples indicates that thermal inertia can be used to estimate thermal conductivity and dry bulk density in the studied area, although such inferences are highly site-specific. Overall, the QTP method holds promise to generate thermal inertia data products and to complement other characterization approaches for advancing understanding of soil properties across far more locations than is currently possible.

Lamb, J. R. [Lawrence Berkeley National Laboratory↗
DOE OSTI2026

Model‐Based Interpretation of Solute Exports and Carbon Partitioning During Shale Weathering in a Mountainous Hillslope

The weathering of sedimentary rocks in high-elevation catchments influences freshwater quality and the global carbon cycle. While individual biogeochemical mechanisms involved in this process are relatively well understood, quantifying their contributions to solute export and carbon fluxes under natural, transient conditions remains challenging. Here, we implement a numerical multidimensional and multiphase model to simulate coupled hydrological and biogeochemical processes in a shale-underlain, snow-dominated hillslope in the Rocky Mountains, Colorado. The model captures the dynamic interplay between soil respiration, mineral weathering, and climate-driven hydrological forcing, reproducing observed soil CO 2 dynamics, groundwater chemistry, and subsurface flow. Our results reveal that seasonal snowmelt enhances carbonate weathering by promoting the infiltration of CO 2 -rich water to depth, while pyrite oxidation is primarily sensitive to low water saturation that facilitates O 2 diffusion through the regolith. Topography modulates the spatial distribution of shale weathering, as steeper slopes enhance lateral drainage, favoring the delivery of reactants to greater depths. While shale weathering at our site acts as a transient carbon sink, with silicates and carbonates buffering acidity and promoting atmospheric CO 2 consumption (1% of soil-derived CO 2 ), the exported dissolved inorganic carbon is predominantly geogenic (∼73%). Consequently, when accounting for long-term marine carbonate precipitation. The current weathering regime represents a net source of carbon to the atmosphere. The oxidation of pyrite and petrogenic organic carbon together release approximately 0.9 mol·m −2 ·yr −1 of CO 2 . Our findings highlight the role of topography, hydroclimate, and the coupling between acid-base reactions in shaping the carbon balance and the solute exports in mountainous critical zones.

carbon cycling↗
DOE OSTI2026

Hydrological and Thermal Dynamics of a Supra‐Permafrost Subterranean Estuary

Subterranean estuaries (STEs), where groundwater interacts with seawater, influence surface and subsurface coastal ecology and biogeochemistry. In Arctic-STEs overlying permafrost, groundwater flow and heat transport determine the fate of organic matter. Yet, direct observations of groundwater flow and heat and solute transport processes in Arctic STEs remain limited. This study characterized groundwater flow paths and fluxes and heat transport within an Arctic-STE along Alaska's Beaufort Sea coast during thawing, summer, and freeze-up. Intertidal seabed temperature-depth profiles collected along a 10-m transect captured the active groundwater flow period, from thaw and flow onset in mid-June to freeze-up in late-September. During this period, aquifer geometry evolved non-uniformly due to spatially varying thaw rates across the STE (mean (m) thaw depths–beach: 0.25 to 0.55–0.6 m on 20 June, 25 July, 1 October; seabed: 0.6–0.9 m from 25 July to 1 October). Groundwater and surface water levels, salinity, and subsurface temperature profiles measured over tidal time scales were interpreted alongside groundwater flow-heat transport numerical simulations. Fresh groundwater discharge was sporadic during thawing (m: 0.32 m 3 /day/m), abundant in summer (m: 0.45 m 3 /day/m), and was largely absent during freeze-up. During freeze-up, groundwater flow was driven exclusively by seawater recirculation via tidal pumping (from thawing to summer to freeze-up: 0.00025–0.15–0.5 m3/day/m) and convection. Heat advection dominated near aquatic interfaces (shaping intertidal ice), and conduction controlled vertical temperature gradients in low-flow and unsaturated sediments. These findings will help predict how prolonged summers will alter Arctic-STE cryo-hydrology and biogeochemistry.

54 ENVIRONMENTAL SCIENCES↗
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↗