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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NSA Site Science: Use of ARM Observations from Northern Alaska to Evaluate and Improve Prediction Capabilities

The Arctic is warming at a rate nearly double that of the rest of the planet, leading to profound changes in atmospheric, oceanic, and ice processes. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility has played a significant role in Arctic research, operating observatories in Alaska's North Slope for over 25 years. These observatories provide a rich, and wide-reaching dataset that offers insight into atmospheric processes in northern Alaska. This report details the results of a nine-year research project (2015–2024) supported by the DOE Atmospheric Systems Research (ASR) program, that leverages data from ARM’s deployment of observing facilities at Utqiaġvik (known as the North Slope of Alaska, or NSA, site) and Oliktok Point, Alaska. The project was conducted in two phases: - Phase 1 (2015–2019): Focused on understanding key atmospheric processes at Oliktok Point, including cloud formation, high-latitude precipitation, aerosol-cloud interactions, and cloud properties. - Phase 2 (2019–2024): Extended the research to the broader North Slope region, using data from both Oliktok Point and Utqiaġvik. Topics explored included surface energy budgets, atmospheric stability, ice nucleation processes, and microphysics in Arctic clouds. The project resulted in numerous research products, including 50 peer-reviewed publications and dissertations, 169 presentations, and 10 data products. These products cover a variety of topics, including: - Cloud Macro- and Microphysical Properties: Arctic clouds play a crucial role in energy transfer, and accurate representation in models is critical. The study explored cloud transitions, ice crystal shapes, and dual-wavelength radar data to understand ice crystal habits and size distributions. - Aerosol Properties and Processes: The team examined aerosol sources in the Arctic, including industrial emissions and natural sources. Observations showed significant spatial gradients in aerosol concentrations due to human activities and wildfire smoke. The influence of aerosols on cloud formation and the surface energy budget was also assessed. - Aerosol-Cloud Interactions: Research revealed that aerosols might suppress cloud ice production, affecting cloud radiative forcing and precipitation. The impact of local industrial emissions on cloud properties was also investigated. - Contextualizing the North Slope of Alaska in the context of the broader Arctic: To understand broader trends, the project evaluated large-scale circulation patterns and the influence of weather systems on the Arctic. Studies indicated that large-scale processes play a significant role in temperature patterns and the timing of snowmelt. - Advancing ARM Observational and Modeling Capabilities: The project developed new radar data products and advanced measurement techniques, including clutter mitigation and drizzle detection. Uncrewed aerial systems (UAS) and tethered balloon systems (TBS) were deployed to gather detailed atmospheric data. Additionally, the project supported 10 early career scientists, providing training and mentorship to undergraduate interns, graduate students, postdoctoral researchers, and early career researchers. These efforts contributed to the advancement of ARM research capabilities and fostered a new generation of scientists skilled in Arctic atmospheric research. Ultimately, this ASR-supported project has provided valuable insights into Arctic atmospheric processes and their broader climate implications. Recommendations for future work include continuing support for long-term observing at Arctic locations to foster additional research, further exploration of aerosol-cloud interactions and the potential impacts of enhanced industrialization of the Arctic, and expanded use of uncrewed systems to gather data in this remote and harsh environment. Additionally, the data products developed by this work, and the data products developed through the ARM infrastructure, leave a treasure-trove of additional information that should be explored for many years to come to gain additional insight into physical processes in the Arctic atmosphere that drive the rapid changes occurring in at high latitudes and their global impact.

58 GEOSCIENCES↗
DOE OSTI2023

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Abstract Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Azimuthally averaged wind and thermodynamic structures of tropical cyclones in global climate models and their sensitivity to horizontal resolution

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. Further, when TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud-radiative feedbacks during the early developmental phases of TCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation

Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a latent heat flux diagnostic that depicts how latent heat fluxes vary with the near-surface specific humidity vertical gradient (Δq) and surface wind speed (|V|). Compared to fluxes estimated from |V| and Δq measured at tropical moorings and the Coupled Ocean Atmosphere Response Experiment 3.0 (COARE3.0) algorithm, tropical latent heat fluxes in the National Center for Atmospheric Research CEMS2 and Department of Energy E3SMv1 models are significantly overestimated at |V| and Δq extrema. Madden–Julian oscillation (MJO) sensitivity to surface flux algorithm is tested with offline and inline flux corrections. The offline correction adjusts model output fluxes toward mooring-estimated fluxes; the inline correction replaces the original bulk flux algorithm with the COARE3.0 algorithm in atmosphere-only simulations of each model. Both corrections indicate reduced latent heat flux feedback to intraseasonal precipitation, in better agreement with observations, suggesting that model-simulated fluxes are overly supportive for maintaining MJO convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Strategies to mitigate urban heat: Effects on overheating and cloud formation

This study evaluates the effectiveness of various urban heat island (UHI) overheating mitigation strategies and their associated impacts on urban cloud dynamics and thermal processes. This study shows cloud-resolving and urban-resolving modeling results estimating the impact of Houston-Galveston heat mitigation scenarios and other resilient strategies contemplated in the Climate Adaptation Plan and Resilient Houston reports. The simulated scenarios include high intensity green rooftops, rooftop photovoltaic solar panels, enhanced urban irrigation, white/cool roofs and roads, and street trees. We contrast the adaptation scenarios against a present baseline case, a no city scenario and a larger and denser city as projected by the Houston-Galveston Area Council for 2045. During the daytime, cooling strategies such as cool roofs, cool roads, and green roofs exhibit superior performance in mitigating urban overheating. At night, enhanced urban irrigation emerges as the most effective cooling intervention. Cooling strategies significantly reduce sensible heat flux partitioning during the day, a process that reduces the uplift of air, suppressing the formation of urban shallow cumulus clouds. The extent of urban cloud mixing ratio is reduced in proportion to the decrease in sensible heating. Under the BEP-Tree scenario, which includes wind effects and evapotranspiration driven by a stomatal conductance model, urban trees demonstrated negligible environmental cooling effects and minimal urban cloud modifications. In contrast, the scenarios with more urban cooling and higher latent heat fluxes led to suppressed urban clouds. The net cooling effect achieved by the heat mitigation strategies is influenced by a combination of indirect processes, including the reduction of downwelling longwave radiation flux, due to reduced cloud presence, while some warming is attributed to a modest increase in shortwave radiation that offsets the cooling benefit. Additionally, reduced heat dissipation, weakened thermal gradients, and diminished vertical mixing over urban areas further moderate the cooling potential. These findings highlight the pivotal role of clouds and moist atmospheric processes in shaping the UHI effect and offer insights for designing more effective urban cooling strategies.

albedo changes↗
DOE OSTI2020

Global biogeography of fungal and bacterial biomass carbon in topsoil

Bacteria and fungi, representing two major soil microorganism groups, play an important role in global nutrient biogeochemistry. Biogeographic patterns of bacterial and fungal biomass are of fundamental importance for mechanistically understanding nutrient cycling. We synthesized 1323 data points of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. The FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic factors. At the biome level, tundra has the highest FBC and BBC densities at 3684 (95% confidence interval: 1678–8084) mg kg -1 and 428 (237–774) mg kg -1 , respectively; desert has the lowest FBC and BBC densities at 16.92 (14.4–19.89) mg kg -1 and 6.83 (6.1–7.65) mg kg -1 , respectively. The F:B ratio varies dramatically, ranging from 1.8 (1.6–2.1) in savanna to 8.6 (6.7–11.0) in tundra. An empirical model was developed for the F:B ratio and it is combined with a global dataset of soil microbial biomass C to produce global maps for FBC and BBC in 0–30 cm topsoil. Across the globe, the highest FBC is found in boreal forest and tundra while the highest BBC is in boreal forest and tropical/subtropical forest, the lowest FBC and BBC are in shrub and desert. Global stocks of living microbial biomass C were estimated to be 12.6 (6.6–16.4) Pg C for FBC and 4.3 (0.5–10.3) Pg C for BBC in topsoil. These findings advance our understanding of the global distribution of fungal and bacterial biomass, which facilitates the incorporation of fungi and bacteria into Earth system models. The global maps of bacterial and fungal biomass serve as a benchmark for validating microbial models in simulating the global C cycle under a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Spatial distributions of X CO 2 seasonal cycle amplitude and phase over northern high-latitude regions

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 (X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72° N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Atlantic Meridional Overturning Circulation’s Response to CO2 Increase: Assessing the Roles of Surface Flux and Oceanic Advection Feedbacks

Abstract The Atlantic meridional overturning circulation (AMOC) is projected to slow down in climate models due to greenhouse gas emissions. However, the physical mechanisms determining the rate of the projected AMOC slowdown remain unclear. Accordingly, this study isolates the roles of oceanic advection and surface flux feedbacks that might accelerate or decelerate the AMOC’s weakening using carbon dioxide (CO 2 ) quadrupling simulations in the CESM1.2 model. Surface flux feedbacks are isolated in partially coupled experiments in which either all surface flux components or the momentum flux responses to AMOC’s weakening that might provide feedback are suppressed, while a tracer decomposition of ocean density anomalies isolates the advection feedbacks. Comparing the ocean density components in the experiments shows that the AMOC’s response is initially determined by CO 2 -induced anomalous surface heat fluxes, and afterward, feedbacks determine its response. In the fully coupled case, surface heat flux feedback strongly promotes AMOC slowdown and causes its near shutdown, while a weaker but active AMOC is maintained when the surface flux feedback is inhibited in the partially coupled case. The positive surface heat flux feedback works by canceling out the negative oceanic heat advection feedback on deep-water formation in the subpolar North Atlantic (SPNA). With the heat advection feedback thus reduced, the positive salinity advection feedback becomes the dominant contributor to SPNA density changes and deep-water formation. In the partially coupled case, negative ocean heat advection feedback and the CO 2 -induced subtropical Atlantic saline anomalies imported into the SPNA play a stabilizing role. The results highlight the importance of SPNA salinity gradients and gyre circulation strength in determining the AMOC’s response rate or recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Identifying geologic characteristics and operational decisions to meet global carbon sequestration goals

Geologic carbon sequestration is the process of injecting and storing CO 2 in subsurface reservoirs and is an essential technology for global environmental security (e.g., climate change mitigation) and economic security (e.g., CO 2 tax credits). To meet energy, economic, and environmental goals, society will have to identify vast volumes of high-capacity, low-cost, and viable storage reservoirs for sequestering CO 2 . In turn, this requires understanding how major geologic characteristics (such as reservoir depth, thickness, permeability, porosity, and temperature) and design and operational decisions (such as injection well spacing) impact CO 2 injection rates, storage capacity, and economics. Although many numerical simulation tools exist, they cannot repeat the required thousands or millions of simulations to identify ideal reservoir properties and the sensitivity and interaction between geologic parameters and operational decisions. Here, we use SCO 2 T—a fast-running, reduced-order modeling framework—to explore the sensitivity of major geologic parameters and operational decisions to engineering (CO 2 injection rates, plume dimensions, and storage capacities and effectiveness) and costs. Our results show, for the first time, benefits and impacts such as allowing CO 2 plumes to overlap, how different well spacing patterns affect CO 2 sequestration, the effects on costs of including brine treatment and disposal, and the effect of restricting injection rates to 1 MtCO 2 per y based on well limitations. We reveal multiple novel and unintuitive findings including: (i) deeper reservoirs have reduced carbon sequestration costs until injection rates reach 1 MtCO 2 per y, at which point deeper reservoirs become more expensive, (ii) thicker formations allow for increased injection rates and storage capacity, but thickness barely impacts plume areas, (iii) higher geothermal gradients result in reduced sequestration costs, unless brine treatment/disposal costs are included, at which point reservoirs having lower geothermal gradients are more economical because they produce less brine for each unit of injected CO 2 , and (iv) allowing plumes to overlap has a significantly positive impact of increasing storage capacities but has only a small influence on reducing sequestration costs. Altogether, our results illustrate new scientific conclusions to help identify suitable sites to inject and store CO 2 , to help understand the complex interaction between geology and resulting costs, and to help support the pursuit of meeting global sequestration targets.

58 GEOSCIENCES↗
DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DISCOVR strain pipeline screening – Part I: Maximum specific growth rate as a function of temperature and salinity for 38 candidate microalgae for biofuels production

Here, to identify high productivity strains for microalgal biofuels generation, the maximum specific growth rate of 38 strains was measured as a function of salinity (i.e., 5, 15, and 35 PSU) and temperature (i.e., at 8 temperatures along a linear gradient from ca. 5 to 45°C) to determine the most suitable growth medium salinity and best growing season, respectively, for outdoor raceway pond cultivation. The following strains were evaluated: Agmenellum quadruplicatum UTEX 2268, Anabaena sp. ATCC 33081, Arthrospira fusiformis UTEX 2721, Arthrospira platensis UTEX 3086, Chlorella vulgaris NREL 4-C12, Chlorella autotrophica CCMP 243, Chlorella sorokiniana DOE1044, Chlorella sorokiniana DOE 1116, Chlorella sorokiniana DOE 1412 (UTEXB3016), Chlorella vulgaris LRB AZ-1201, Chlorococcum littorale UTEX 117, Chlorococcum sp. UTEX-B P7, Chloromonas reticulata CCALA 870, Coelastrella sp. DOE 0202, Cyanobacterium sp. AB1, Micractinium reisseri NREL 14-F2, Microchloropsis gaditana CCMP1894, Microchloropsis salina CCMP 1776, Monoraphidium sp. MONOR1, Monoraphidium minutum 26B-AM, Nannochloropsis oceanica CCAP 849/10, Oscillatoria cf. priestleyi CCMEE 5020.1-1, Picochlorum celeri TG2-WT-CSM/EMRE, Picochlorum oklahomensis CCMP 2329, Picochlorum renovo NREL 39-A8, Picochlorum soloecismus DOE 101, Porphyridium cruentum CCMP 675, Scenedesmus acutus LRB-AP-0401, Scenedesmus obliquus DOE 0152.z, Scenedesmus obliquus UTEX393,Scenedesmus rubescens NREL 46B-D3, Scenedesmus sp. IITRIND2, Stichococcus minor CCMP 819, Stichococcus minutus CCALA 727, Synechococcus elongatus UTEX2973.1, Tetraselmis striata LANL 1001, Tisochrysis lutea CCMP 1324, and Tribonema minus UTEXB3156. For each strain, the identity and the presence of bacterial cohorts was determined using 18S and 16S rDNA sequencing, respectively. The maximum specific growth rate versus temperature data were also used to determine the activation energies (Arrhenius equation) for most strains. For all strains, the measured salinity and temperature tolerance data were compared to those reported in the literature. The fastest growing strains were down-selected for subsequent biomass productivity measurements in climate-simulation photobioreactors, as reported in the next paper in the issue.

18S and 16S rDNA sequencing↗
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Exploring the elevated water vapor signal associated with the free tropospheric biomass burning plume over the southeast Atlantic Ocean

In southern Africa, widespread agricultural fires produce substantial biomass burning (BB) emissions over the region. The seasonal smoke plumes associated with these emissions are then advected westward over the persistent stratocumulus cloud deck in the southeast Atlantic (SEA) Ocean, resulting in aerosol effects which vary with time and location. Much work has focused on the effects of these aerosol plumes, but previous studies have also described an elevated free tropospheric water vapor signal over the SEA. Water vapor influences climate in its own right, and it is especially important to consider atmospheric water vapor when quantifying aerosol-cloud interactions and aerosol radiative effects. Here we present airborne observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign over the SEA Ocean. In observations collected from multiple independent instruments on the NASA P-3 aircraft (from near-surface to 6-7 km), we observe a strongly linear correlation between pollution indicators (carbon monoxide (CO) and aerosol loading) and atmospheric water vapor content, seen at all altitudes above the boundary layer. The focus of the current study is on the especially strong correlation observed during the ORACLES-2016 deployment (out of Walvis Bay, Namibia), but a similar relationship is also observed in the August 2017 and October 2018 ORACLES deployments. Using reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and specialized WRF-Chem simulations, we trace the plume-vapor relationship to an initial humid, smoky continental source region, where it mixes with clean, dry upper tropospheric air and then is subjected to conditions of strong westward advection, namely the southern African easterly jet (AEJ-S). Our analysis indicates that air masses likely left the continent with the same relationship between water vapor and carbon monoxide as was observed by aircraft. This linear relationship developed over the continent due to daytime convection within a deep continental boundary layer (up to ~5-6 km) and mixing with higher-altitude air, which resulted in fairly consistent vertical gradients in CO and water vapor, decreasing with altitude and varying in time, but this water vapor does not originate as a product of the BB combustion itself. Due to a combination of conditions and mixing between the smoky, moist continental boundary layer and the dry and fairly clean upper-troposphere air above (~6 km), the smoky, humid air is transported by strong zonal winds and then advected over the SEA (to the ORACLES flight region) following largely isentropic trajectories. Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) back trajectories support this interpretation. This work thus gives insights into the conditions and processes which cause water vapor to covary with plume strength. Better understanding of this relationship, including how it varies spatially and temporally, is important to accurately quantify direct, semi-direct, and indirect aerosol effects over this region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
DOE OSTI2023

The evolution of soil microstructure and micromineralogy along a soil age gradient on the Galápagos Islands (Ecuador)

The islands of the Galápagos archipelago are of different ages due to the combination of a geostationary volcanic hotspot and the eastward movement of the Nazca tectonic plate. Taking advantage of this, a soil chronosequence (1.5–1070 ka) has recently been established covering four of the islands. This was used to investigate the temporal development of soil microstructure and micromineralogy using micromorphological methods in combination with synchrotron-based mineralogical and geochemical microanalyses. Along the chronosequence, the microstructure changed from intergrain microaggregates in the young soils to angular blocky microaggregates in the older soils. In the young soils, a high amount of vitric scoria and weatherable minerals occur, but they quickly disappear with age, and micromass increased fast. The micromorphological studies as well as micro-XRD and micro-XRF revealed increasing heterogeneity of soil microstructure and micromineralogy due to weathering and pedogenic processes until intermediate soil age. In the oldest soils, the microstructure became more uniform again, de-mixing pedofeatures like clay coatings have completely disappeared, and kaolinite, hematite and gibbsite dominate the mineralogical composition. Our study shows that the development of soil microstructure mirrors macroscopic soil profile development showing increasing differentiation until intermediate developmental stages and receding heterogeneity in highly weathered soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Temporal, Spatial, and Temperature Controls on Organic Carbon Mineralization and Methanogenesis in Arctic High-Centered Polygon Soils

Warming temperatures in continuous permafrost zones of the Arctic will alter both hydrological and geochemical soil conditions, which are strongly linked with heterotrophic microbial carbon (C) cycling. Heterogeneous permafrost landscapes are often dominated by polygonal features formed by expanding ice wedges: water accumulates in low centered polygons (LCPs), and water drains outward to surrounding troughs in high centered polygons (HCPs). These geospatial differences in hydrology cause gradients in biogeochemistry, soil C storage potential, and thermal properties. Presently, data quantifying carbon dioxide (CO 2 ) and methane (CH 4 ) release from HCP soils are needed to support modeling and evaluation of warming-induced CO 2 and CH 4 fluxes from tundra soils. This study quantifies the distribution of microbial CO 2 and CH 4 release in HCPs over a range of temperatures and draws comparisons to previous LCP studies. Arctic tundra soils were initially characterized for geochemical and hydraulic properties. Laboratory incubations at −2, +4, and +8°C were used to quantify temporal trends in CO 2 and CH 4 production from homogenized active layer organic and mineral soils in HCP centers and troughs, and methanogen abundance was estimated from mcrA gene measurements. Results showed that soil water availability, organic C, and redox conditions influence temporal dynamics and magnitude of gas production from HCP active layer soils during warming. At early incubation times (2–9 days), higher CO 2 emissions were observed from HCP trough soils than from HCP center soils, but increased CO 2 production occurred in center soils at later times (>20 days). HCP center soils did not support methanogenesis, but CH 4 -producing trough soils did indicate methanogen presence. Consistent with previous LCP studies, HCP organic soils showed increased CO 2 and CH 4 production with elevated water content, but HCP trough mineral soils produced more CH 4 than LCP mineral soils. HCP mineral soils also released substantial CO 2 but did not show a strong trend in CO 2 and CH 4 release with water content. Knowledge of temporal and spatial variability in microbial C mineralization rates of Arctic soils in response to warming are key to constraining uncertainties in predictive climate models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-June 2024. (DE-SC0021139)

This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data, a row of column descriptors, and units of measurements. some data processing and QA/QC was done to filter out data from sensors that went bad and extreme outliers. CO2 sensors that went bad were replaced with new sensors as soon as possible. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input. This is the second version of this data set and was modified on 10/01/2024. The primary change in the data was the addition of data from the fall of 2022 to June of 2024. In addition, minor QA/QC was done to filter out data from sensors that went bad and extreme outliers. THe filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. This is now the third version of the data set, and was modified 03/25/2026. The primary change in the data was the addition of data from the June of 2024 to December 2025. Further r QA/QC was done with the new data to filter out bad data from faulty sensors and extreme outliers. The filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. ##This additional data was funded under DE-SC0024218( Responses of Plant and Microbial Respiration Sources to Changing Cold Season Climate Drivers in the East River Watershed)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗