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DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. 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.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗

DOE OSTI2020

Optimal experimental design for prediction based on push-forward probability measures

Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the pushforward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Cr plasma-material-interaction in PISCES-RF: D thermal release, retention, and erosion

Pure chromium (Cr) targets were exposed to high-flux deuterium (D) plasmas in the Pisces-RF linear plasma device, and measurements of D retention, release, and erosion were subsequently performed. Post-exposure D retention was quantified using temperature-programmed desorption on Cr targets irradiated by 50 eV ions over a broad range of exposure temperatures (423–873 K) and ion fluences (3 × 10 24 – 3 × 10 26 m −2 ). The retained D inventory was observed to decrease rapidly with increased exposure temperature, from approximately ∼7 × 10 20 m −2 at ∼ 420 K, to then saturate near ∼ 10 20 m −2 for exposure temperatures above ∼550 K. Separately, at fixed exposure temperature (∼450 K), D retention was found to have only a weak dependence on increasing ion fluence. Lastly, the erosion of Cr in D plasma was investigated for ion impact energies in the range 40 ≤ E i ≤ 250 eV. Erosion was inferred using optical emission spectroscopy (OES) from the ratio of emission lines (Cr I (425.4 nm)/D I (656.1 nm)) measured close to the target. Conversion of the OES yield data to net erosion yield was made with singular ion energy target mass-loss measurements. These net erosion yield data were then further corrected to obtain gross erosion yield by accounting for a re-deposition factor, computed using a simple model. The gross erosion yield is found to be 2–4 times lower than predicted by SDTrimSP, consistent with that typically observed for light-ion sputtering under high-flux plasma conditions.

Chromium↗

DOE OSTI2021

Top-level physics requirements and simulated performance of the MRSt on the National Ignition Facility

The time-resolving Magnetic Recoil Spectrometer (MRSt) for the National Ignition Facility (NIF) has been identified by the US National Diagnostic Working Group as one of the transformational diagnostics that will reshape the way inertial confinement fusion (ICF) implosions are diagnosed. The MRSt will measure the time-resolved neutron spectrum of an implosion, from which the time-resolved ion temperature, areal density, and yield will be inferred. Top-level physics requirements for the MRSt were determined based on simulations of numerous ICF implosions with varying degrees of alpha heating, P2 asymmetry, and mix. Synthetic MRSt data were subsequently generated for different configurations using Monte–Carlo methods to determine its performance in relation to the requirements. The system was found to meet most requirements at current neutron yields at the NIF. This work was supported by the DOE and LLNL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Informational approach to cosmological parameter estimation

Here, we introduce a new approach for cosmological parameter estimation based on the information-theoretical Jensen-Shannon divergence ($\mathscr{D}$ JS ), calculating it for models in the restricted parameter space {H 0 , w 0 , w a }, where H 0 is the value of the Hubble constant today, and w 0 and w a are dark energy parameters, with the other parameters held fixed at their best-fit values from the Planck 2018 data. As an application, we investigate the H 0 tension between the Planck temperature power spectrum data (TT) and the local astronomical data by comparing the ΛCDM model with the wCDM and the w 0 w a CDM dynamic dark energy models. We find agreement with other works using the standard Bayesian inference for parameter estimation; in addition, we show that while the $\mathscr{D}$ JS is equally minimized for both values of H 0 along the (w 0 , w a ) plane, the lines of degeneracy are different for each value of H 0 . This allows for distinguishing between the two, once the value of either w 0 or w a is known.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗

DOE OSTI2021

Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation

Genomes reveal nutrient stress patterns Within the surface ocean, nitrogen, iron, and phosphorous can all be limiting nutrients for phytoplankton depending on location. Ustick et al. used the prevalence of Prochlorococcus genes involved in nutrient acquisition to develop maps of inferred nutrient stress across the global ocean (see the Perspective by Coleman). They found broad patterns of limitation consistent with an Earth system model and nutrient addition experiments. Leveraging metagenomic data in this manner is an appealing approach that will help to expand our understanding of the biogeochemistry in the vast open ocean. Science , this issue p. 287 ; see also p. 239

Science & Technology - Other Topics↗

DOE OSTI2024

CHMMPP: A c++ library for constrained Hidden Markov Models

SAND2024-13027O The CHMMPP: A c++ Library for Constrained Hidden Markov Models (HMM) software supports the analysis of multivariate time series data to detect patterns using HMM. Many applications involve the detection and characterization of hidden or latent states in a complex system using observable states and variables. This software supports inference of latent states integrating both an HMM and application-specific constraints that reflect known relationships in hidden states. The CHMMPP software supports application-specific and generic methods for constrained inference. This includes a framework for customized Viterbi methods, constrained inference of hidden states with A* and integer programming methods, and various constraint-informed methods for learning HMM model parameters. CHMMPP focuses on supporting generic methods that enable the agile expression of complex sets of constraints that naturally arise in many real-world applications.

Hart, William↗

DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗

DOE OSTI2024

The Escape Velocity Profile of the Milky Way from Gaia DR3

Abstract The escape velocity profile of the Milky Way offers a crucial and independent measurement of its underlying mass distribution and dark matter (DM) properties. Using a sample of stars from the third data release of Gaia with 6D kinematics and strict quality cuts, we obtain an escape velocity profile of the Milky Way from 4 to 11 kpc in Galactocentric radius. To infer the escape velocity in radial bins, we model the tail of the stellar speed distribution with both traditional power-law models and a new functional form that we introduce. While power-law models tend to rely on extrapolation to high speeds, we find our new functional form gives the most faithful representation of the observed distribution. Using this for the escape velocity profile, we constrain the properties of the Milky Way’s DM halo modeled as a Navarro–Frenck–White profile. Combined with constraints from the circular velocity at the solar position, we obtain a concentration and mass of c 200 c DM = 13.9 − 4.3 + 6.2 and M 200 c DM = 0.55 − 0.14 + 0.15 × 10 12 M ⊙ . This corresponds to a total Milky Way mass of M 200 c = 0.64 − 0.14 + 0.15 × 10 12 M ⊙ , which is on the low end of the historic range of the galaxy’s mass, but in line with other recent estimates.

Astronomy & Astrophysics↗

DOE OSTI2026

The DECam MAGIC Survey: Uncovering the Tidal Tails of the Crater II Dwarf Galaxy

Crater II (CraII), a large and low-density dwarf spheroidal galaxy, has unusual observed properties that are difficult to reproduce in cold dark matter simulations. Ongoing tidal disruption may help explain the discrepancies, as evidenced by the recent discovery of tidal tails. Here we present metallicity-sensitive narrowband photometry of the Ca II H and K lines from the Dark Energy Camera, covering 128 deg$^{2}$ across the center and identified tidal tails of CraII as part of the Mapping the Ancient Galaxy in CaHK survey. Our combined photometric metallicity, color–magnitude, proper motion, and parallax selections identify 162 CraII candidates. Of these, 37 candidates are located in the tidal tails, which extend at least 7° (∼95 kpc) from the center of CraII, suggesting it has lost ≳25% of its initial stellar mass. We confirm low contamination rates with dedicated control fields and highlight the extremely low surface brightness stellar features that can be uncovered with CaHK data, as faint as ∼36 mag arcsec$^{−2}$. We also make the first detection of a metallicity gradient (−0.34 ± 0.17 dex deg$^{−1}$) in the center of the galaxy and infer a stream width of w ∼ 0.°8, roughly 50% larger than the CraII half-light radius. The detection of candidates in the most distant CraII pointings from its center implies that the tidal tails extend beyond our footprint. We compare the CraII stream to N-body models with “cored” and “cuspy” dark matter halo progenitors, determining that CraII’s density profile is still ambiguous and warrants further modeling.

Atzberger, Kaia R. [Virginia U., Astron. Dept.] (O↗

DOE OSTI2021

Top-level physics requirements and simulated performance of the MRSt on the National Ignition Facility

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

The Sixth Data Release of the Radial Velocity Experiment (Rave). II. Stellar Atmospheric Parameters, Chemical Abundances, and Distances

We present part 2 of the sixth and final Data Release (DR6) of the Radial Velocity Experiment (Rave), a magnitude-limited (9<I<12) spectroscopic survey of Galactic stars randomly selected in Earth’s southern hemisphere. The Rave medium-resolution spectra (R ∼ 7500) cover the Ca triplet region (8410–8795 Å) and span the complete time frame from the start of Rave observations on 2003 April 12 to their completion on 2013 April 4. In the second of two publications, we present the data products derived from 518,387 observations of 451,783 unique stars using a suite of advanced reduction pipelines focusing on stellar atmospheric parameters, in particular purely spectroscopically derived stellar atmospheric parameters (T{sub eff}, logg, and the overall metallicity), enhanced stellar atmospheric parameters inferred via a Bayesian pipeline using Gaia DR2 astrometric priors, and asteroseismically calibrated stellar atmospheric parameters for giant stars based on asteroseismic observations for 699 K2 stars. In addition, we provide abundances of the elements Fe, Al, and Ni, as well as an overall [α/Fe] ratio obtained using a new pipeline based on the GAUGUIN optimization method that is able to deal with variable signal-to-noise ratios. The Rave DR6 catalogs are cross-matched with relevant astrometric and photometric catalogs, and are complemented by orbital parameters and effective temperatures based on the infrared flux method. The data can be accessed via the Rave website (http://rave-survey.org) or the Vizier database.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗

DOE OSTI2025

A surprising proliferation of detwinning in β -tin at extreme loading rates

Integrating data from dynamic compression experiments of condensed matter across three national laboratories has led to insight and quantitative calibration of materials strength over decades of loading rate. For many materials, a single strength model (such as PTW) is sufficient to capture the flow-stress strain rate relationship which is monotonic. Here, we show here that β -tin, a tetragonal metal, exhibits dramatic deviations from this behavior. Naive fitting to a single PTW model is insufficient to capture the behavior; indeed, such resulting inferred flow stress versus strain exhibits a non-monotonic behavior. We suggest a resolution to this by proposing that in β -tin there are important Bauschinger effects arising from favorable conditions for twinning and detwinning. A simple yield surface model when paired with PTW hardening captures the experimental data.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗