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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Changes in Aerosols, Meteorology, and Radiation in the Southeastern U.S. Warming Hole Region during 2000 to 2019

Abstract Surface air temperatures in the southeastern United States that did not change from the climatological mean from 1900 to 2000 have increased since the year 2000. Analyzed herein are factors modulating the surface air temperatures in the region for a 20-yr period (2000–19) using space- and surface-based observations, and output from a reanalysis model. The 20-yr period is segregated into two decades, 2000–09 and 2010–19, corresponding to different tropospheric chemical regimes. Changes in seasonal and decadal averages are examined. The later decade experienced higher average surface air temperatures with significant warming during summer and fall seasons. Decadal and seasonal averages of cloud properties, column water vapor, rain rates, and top-of-atmosphere outgoing longwave radiation did not exhibit statistically significant differences between the two decades. The region experienced strong warm and moist advection during the winter months and very weak advection during the summer months. The later decade exhibited higher low-level moisture advection during the winter months than the earlier decade with insignificant changes in the temperature advection between the two decades. The later decade had significantly lower aerosol dry and liquid water mass during all seasons, along with lower aerosol optical depth, higher single scattering albedo, and lower top-of-the-atmosphere outgoing shortwave radiation during cloud-free conditions in the summer season. Collectively, these results suggest that changes in the aerosol direct radiative forcing are responsible for warming during summer months that experience weak advection and highlight seasonal differences in the temperature controlling mechanisms in the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Weak influence of anthropogenic emissions on aerosol, cloud, and rain in the wet season of the Amazon rainforest

Anthropogenic emissions have been shown to affect new particle formation, aerosol concentrations, and clouds. Such effects vary with region, environmental conditions, and cloud types. In the wet season of Amazonia, anthropogenic emissions emitted from Manaus, Brazil, can significantly increase the cloud condensation nuclei (CCN) concentrations compared to the background of mainly natural aerosols. However, the regional response of cloud and rain to anthropogenic emissions in Amazonia remains very uncertain. Here, we aim to quantify how aerosol concentration, cloud, and rain respond to changes in anthropogenic emissions through parameterized new particle formation and primary aerosol emission in the Manaus region and to understand the underlying mechanisms. We ran the atmosphere-only configuration of the HadGEM3 climate model with a nested regional domain that covers most of the rainforest region (720 km by 1200 km with 3 km resolution) under scaled regional emissions. The 7 d simulations show that, in the areas that are affected by anthropogenic emissions, when aerosol and precursor gas emissions are doubled from the baseline emission inventories, aerosol number concentrations increase by 13 %. The nucleation rate that involves sulfuric acid and biogenic compounds generally increases with pollution levels. However, nucleation is suppressed very close to the pollution source, resulting in lower nucleation and soluble Aitken mode aerosol number concentrations. We also found that doubling the anthropogenic emission can increase the cloud droplet number concentrations ( N d ) by 9 %, but cloud water and rain mass mixing ratios do not change significantly. Even very strong reductions in aerosol number concentrations by a factor of 4, which is an unrealistic condition, cause only a 4 % increase in rain over the domain. If we assume our simulation has a fine enough grid resolution and an accurate representation of the relevant atmospheric processes, the simulated weak and non-linear response of cloud and rain properties to linearly scaled anthropogenic emissions suggests that the interactions among aerosol, cloud, and precipitation in the Amazonian convective environment are buffered by microphysical processes. It also implies that the convective environment is resilient to the changes in Nd that occur in response to localized anthropogenic aerosol perturbations.

Wang, Xuemei [University of Leeds (United Kingdom)↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Model Intercomparison of the Impacts of Varying Cloud Droplet–Nucleating Aerosols on the Life Cycle and Microphysics of Isolated Deep Convection

The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas, on 19 June 2013, are examined using multiple cloud-resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet–nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell-tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 min). Most of the CRMs produced similar aerosol loading impacts on the warm phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the life cycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable among CRMs and included increases or decreases in ice amounts at inconsistent stages of the cell life cycle and midlevel versus upper-level changes in ice. This intermodel variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations.

Aerosol-cloud interaction↗
DOE OSTI2025

Widespread underestimation of rain-induced soil carbon emissions from global drylands

Dryland carbon fluxes, particularly those driven by ecosystem respiration, are highly sensitive to water availability and rain pulses. However, the magnitude of rain-induced carbon emissions remains unclear globally. Here we quantify the impact of rain-pulse events on the carbon balance of global drylands and characterize their spatiotemporal controls. Using eddy-covariance observations of carbon, water and energy fluxes from 34 dryland sites worldwide, we produce an inventory of over 1,800 manually identified rain-induced CO2 pulse events. Based on this inventory, a machine learning algorithm is developed to automatically detect rain-induced CO2 pulse events. Our findings show that existing partitioning methods underestimate ecosystem respiration and photosynthesis by up to 30% during rain-pulse events, which annually contribute 16.9 ± 2.8% of ecosystem respiration and 9.6 ± 2.2% of net ecosystem productivity. We show that the carbon loss intensity correlates most strongly with annual productivity, aridity and soil pH. Finally, we identify a universal decay rate of rain-induced CO2 pulses and use it to bias-correct respiration estimates. Our research highlights the importance of rain-induced carbon emissions for the carbon balance of global drylands and suggests that ecosystem models may largely underrepresent the influence of rain pulses on the carbon cycle of drylands.

Nguyen, Ngoc B↗
DOE OSTI2024

Energy‐Water Asynchrony Principally Determines Water Available for Runoff From Snowmelt in Continental Montane Forests

ABSTRACT Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r 2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r 2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.

Webb, Ryan William↗
DOE OSTI2023

Rapid saturation of cloud water adjustments to shipping emissions

Abstract. Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid-water content and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modeled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h−1) before the response saturates. Liquid water increases in raining clouds, and the anomalies are constant over the emission ranges observed. There is evidence that this independence of emissions is due to compensating effects under drier and more humid conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid-water responses to aerosol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)

Abstract. Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

BIRDIE beryllium-10 and -7 measurements from 2022 summer deep convection rain at ARM SGP

Water samples were manually collected from 11 SGP ARM sites in the early summer of 2022 to measure cosmogenic isotopes of beryllium. The water samples were brought back to the Center for Accelerator Mass Spectrometry (CAMS) at LLNL and analysed for beryllium-10 and beryllium-7 as well as for chlorine-37, chlorine-36, and tritium. A total of six storm events were measured over three weeks. At least five of those storms included overshooting cloud events. Available data include precipitation amount, beryllium-10 concentration, beryllium-7 concentration, and 10/7Be ratio. Spatial and temporal variability in the cosmogenic isotopes was relatively high but could not directly be linked to precipitation amount. Analysis is on-going to explain the measured variability and to see if it can be linked to cloud top height and overshooting depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Clarifying remotely-retrieved precipitation of shallow marine clouds from the NSF/NCAR Gulfstream V

Precipitation is a key process within the shallow cloud lifecycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94 GHz Doppler radar and 532 nm lidar. Despite a larger sampling volume, initial mean radar/lidar retrieved rain rates (Schwartz et al. 2019) based on the upward-pointing remote sensor datasets are systematically less than those measured by in-situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rainrates that compare better to in-situ values, but still underestimate. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the dropsize distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the dropsizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different dropsize representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the dropsize distribution width based on the in-situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Light rain exacerbates extreme humid heat

Abstract Humid heat waves pose significant risks to human health and the ecosystem. Intuitively, rainfall often alleviates extreme humid heat. However, here we show that light rain often accompanies extreme humid heat, exacerbating its frequency and intensity, especially over arid and semi-arid regions compared to no rain and moderate-to-heavy rain cases. This is because light rain does not dramatically reduce solar radiation but increases near-surface humidity through enhanced surface evaporation. The water replenishment from light rain as well as a shallower planetary boundary layer is crucial for consecutive extremes where there are commonly sporadic drizzle days amidst several rain-free days. These extremes last longer than rain-free extremes. Current global climate models (GCMs) overestimate light rain. After reducing this bias in a GCM, underestimations of humid heat waves in energy-limited regions and overestimations in water-limited regions are largely alleviated. These findings underscore the underappreciated impact of light rain on extreme humid heat.

Science & Technology - Other Topics↗
DOE OSTI2025

Flower‐Type Organized Trade‐Wind Cumulus: A Multi‐Day Lagrangian Large Eddy Simulation Intercomparison Study

Shallow cumulus cloud fields in subtropical marine trade wind environments, particularly over the tropical Atlantic Ocean, show distinct organizational patterns. Among these, Flower‐type clouds are characterized by expansive stratiform cloud patches surrounded by regions of scattered convection. The objectives of this study were (a) to construct a case study of a time period during the EUREC 4 A/ATOMIC field campaign when Flower‐type organization was observed, (b) to evaluate the fidelity of a multi‐model ensemble of large eddy simulations of that case, and (c) to analyze the interaction between cloud and precipitation processes and mesoscale organization in the simulations. The simulations follow a quasi‐Lagrangian trajectory, allowing mesoscale features to develop over time in a domain that follows the boundary‐layer airmass. The results show a broad agreement in simulated thermodynamic properties across different LES codes, with Flower‐type cloud patches appearing within hours of each other. The consensus among models is consistent with observations made during the EUREC 4 A/ATOMIC field campaign on the specific day of interest. The cloud structure reveals three distinct peaks in the joint probability densities of cloud base and cloud top height, with the dominant peak at any given time influenced by the stage of cloud organization. The simulated cloud system evolution reveals consistent occurrence of maxima in liquid water path and rain rate before Flower reaches its maximum length scale. Targeted sensitivity tests reveal a weak relationship between Cloud Droplet Number concentration and the extent/degree/type of organization.

EUREC4A↗
DOE OSTI2023

Mesoscale convective systems in the third pole region: Characteristics, mechanisms and impact on precipitation

The climate system of the Third Pole region, including the (TP) and its surroundings, is highly sensitive to global warming. Mesoscale convective systems (MCSs) are understood to be a vital component of this climate system. Driven by the monsoon circulation, surface heating, and large-scale and local moisture supply, they frequently occur during summer and mostly over the central and eastern TP as well as in the downstream regions. Further, MCSs have been highlighted as important contributors to total precipitation as they are efficient rain producers affecting water availability (seasonal precipitation) and potential flood risk (extreme precipitation) in the densely populated downstream regions. The availability of multi-decadal satellite observations and high-resolution climate model datasets has made it possible to study the role of MCSs in the under-observed TP water balance. However, the usage of different methods for MCS identification and the different focuses on specific subregions currently hamper a systematic and consistent assessment of the role played by MCSs and their impact on precipitation over the TP headwaters and its downstream regions. Here, we review observational and model studies of MCSs in the TP region within a common framework to elucidate their main characteristics, underlying mechanisms, and impact on seasonal and extreme precipitation. We also identify major knowledge gaps and provide suggestions on how these can be addressed using recently published high-resolution model datasets. Three important identified knowledge gaps are 1) the feedback of MCSs to other components of the TP climate system, 2) the impact of the changing climate on future MCS characteristics, and 3) the basin-scale assessment of flood and drought risks associated with changes in MCS frequency and intensity. A particularly promising tool to address these knowledge gaps are convection-permitting climate simulations. Therefore, the systematic evaluation of existing historical convection-permitting climate simulations over the TP is an urgent requirement for reliable future climate change assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Optical properties and composition of viscous organic particles found in the Southern Great Plains

Abstract. Atmospheric high-viscosity organic particles (HVOPs) were observed in samples of ambient aerosols collected in April and May 2016 in the Southern Great Plains of the United States. These particles were apportioned as either airborne soil organic particles (ASOPs) or tar balls (TBs) from biomass burning based on spetro-microscopic imaging and assessments of meteorological records of smoke and precipitation data. Regardless of their apportionment, the number fractions of HVOPs were positively correlated (R2=0.85) with increased values of absorption Ångström exponent (AAE) measured in situ for ambient aerosol at the site. Extending this correlation to 100 % HVOPs yields an AAE of 2.6, similar to previous literature reports of the class of light-absorbing organic particles known as brown carbon (BrC). One out of the three samples investigated had a significant number of ASOPs, while the other two samples contained TBs. Although there are chemical similarities between ASOPs and TBs, they can be distinguished based on composition inferred from near-edge absorption X-ray fine structure (NEXAFS) spectroscopy. ASOPs were distinguished from TBs based on their average -COOH/C=C and -COOH/COH peak ratios, with ASOPs having lower ratios. NEXAFS spectra of filtered soil organic brine particles nebulized from field samples of standing water deposited after rain were consistent with ASOPs when laboratory particles were generated by bubble bursting at the air–organic brine interface. However, particles generated by nebulizing the bulk volume of soil organic brine had a particle composition different from ASOPs. These observations are consistent with the raindrop generation mechanism responsible for ASOP emissions in the area of study. In contrast, nebulized samples carry with them higher fractions of soil inorganics dissolved in the bulk volume of soil brine, which are not aerosolized by the raindrop mechanism. Our results support the bubble bursting mechanism of particle generation during rainfall resulting in the ejection of soil organics into the atmosphere. In addition, our results show that ASOPs may only be atmospherically relevant during times when suitable emission conditions are met.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Drizzle, Turbulence, and Density Currents Below Post Cold Frontal Open Cellular Marine Stratocumulus Clouds

Nine cases of postcold frontal marine stratocumulus clouds exhibiting open cellular mesoscale organization are analyzed to characterize the drizzle, turbulence, and density currents below them. Data collected by the vertically pointing Doppler cloud radar and multiple lidars part of the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site are used in these analyses. A total of 251 drizzle shafts passed over the site with 76 shafts sampled for more than 15 min by the vertically pointing instruments. On average the drizzle shafts were ~18 km wide with below-cloud drizzle water path of ~40 g m −2 , cloud base rain rate of 7.78 mm day −1 , and cloud base drizzle modal diameter of 393 μm. The widths of the drizzle shafts did not exhibit any relationship with the cloud base rain rate, below-cloud drizzle water path, and the total water removed from the cloud. The downdrafts in the lowest 500 m within the drizzle cells strengthened with increasing cloud base rain rates, while the updrafts within drizzle cells did not exhibit this behavior. On average, the surface air density, pressure, water vapor mixing ratio, and wind speed along the background wind during the drizzle shaft were higher by 1.88 g m −3 , 8.60 Pa, 0.24 g kg −1 , and 1.22 m s −1 , respectively, from their background values. On the other hand, the surface air temperature was lower by 0.43 K during the drizzle shaft than its background value. The coincident measurements highlight the drizzle-turbulence-surface coupling in these cloud systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Distinct xylem responses to acute vs prolonged drought in pine trees

Increasing dryness challenges trees’ ability to maintain water transport to the leaves. Most plant hydraulics models use a static xylem response to water stress. Yet, in reality, lower soil moisture and warmer temperatures during growing seasons feed back onto xylem development. In turn, adjustments to water stress in the newly built xylem influence future physiological responses to droughts. In this study, we investigate the annual variation of anatomical traits in branch xylem in response to different soil and atmospheric moisture conditions and tree stress levels, as indicated by seasonal predawn leaf water potential (??L,pd). We used a 6-year field experiment in southwestern USA with three soil water treatments applied to Pinus edulis Engelm trees—ambient, drought (45% rain reduction) and irrigation (15–35% annual water addition). All trees were also subject to a natural 1-year acute drought (soil and atmospheric) that occurred during the experiment. The irrigated trees showed only moderate changes in anatomy-derived hydraulic traits compared with the ambient trees, suggesting a generally stable, well-balanced xylem structure under unstressed conditions. The artificial prolonged soil drought increased hydraulic efficiency but lowered xylem construction costs and decreased tracheid implosion safety ((t/b)2), suggesting that annual adjustments of xylem structure follow a safety–efficiency trade-off. The acute drought plunged hydraulic efficiency across all treatments. The combination of acute and prolonged drought resulted in vulnerable and inefficient new xylem, disrupting the stability of the anatomical trade-off observed in the rest of the years. The xylem hydraulic traits showed no consistent direct link to ??L,pd. In the future, changes in seasonality of soil and atmospheric moisture are likely to have a critical impact on the ability of P. edulis to acclimate its xylem to warmer climate. Furthermore, the increasing frequency of acute droughts might reduce hydraulic resilience of P. edulis by repeatedly creating vulnerable and less efficient anatomical structure.

Guerin, Marceau↗
DOE OSTI2026

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability"

The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗