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DOE OSTI2016

Further Insights on the Influence of the Indian Ocean Dipole on the Following Year’s ENSO from Observations and CMIP5 Models

Recent observational studies have suggested that negative and positive Indian Ocean dipole (IOD) events (nIOD and pIOD, respectively) favor a transition toward, respectively, El Niño and La Niña events one year later. These statistical inferences are however limited by the length and uncertainties in the observational records. This paper compares observational datasets with twenty-one 155-yr historical simulations from phase 5 of CMIP (CMIP5) to assess IOD and El Niño–Southern Oscillation (ENSO) properties along with their synchronous and delayed relationships. In the observations and most CMIP5 models, it is shown that El Niños tend to be followed by La Niñas but not the opposite, that pIODs co-occur more frequently with El Niños than nIODs with La Niñas, that nIODs tend to be followed by El Niños one year later less frequently than pIODs by La Niñas, and that including an IOD index in a linear prediction based on the Pacific warm water volume improves ENSO peak hindcasts at 14 months lead. The IOD–ENSO delayed relationship partly results from a combination of ENSO intrinsic properties (e.g., the tendency for El Niños to be followed by La Niñas) and from the synchronous IOD–ENSO relationship. The results, however, reveal that this is not sufficient to explain the high prevalence of pIOD–Niña transitions in the observations and 75% of the CMIP5 models, and of nIOD–Niño transitions in 60% of CMIP5 models. Lastly, this suggests that the tendency of IOD to lead ENSO by one year should be explained by a physical mechanism that, however, remains elusive in the CMIP5 models. The ability of many CMIP5 models to reproduce the delayed influence of the IOD on ENSO is nonetheless a strong incentive to explore extended-range dynamical forecasts of ENSO.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Moving beyond the total sea ice extent in gauging model biases

Here, reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1

This dataset presents a suite of high-resolution fire weather index datasets calculated from observation (gridMet, Livneh, Daymet V4), reanalysis (AgERA5), downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Aside from the daily FWI datasets, we also include a set of FWI extreme indicators at annual, seasonal, and monthly scales, including 1) fwixx: maximum FWI; 2) fwisa: mean FWI. For annual fwisa, it refers to the season with the maximum seasonal average; 3) fwils: Length of fire season over a specified period, where fire season is defined as the days exceeding the median value of the normalized FWI during the reference period (1980-1984); 4) fwixd: Number of extreme fire weather days over a specified period, where extreme day is defined as the day with FWI > the 95th percentile of the FWI during the reference period (1980-1984). All FWI datasets cover 1980-2020 baseline and the model simulated products including the downscaled products additionally include 2021-2099 near-future periods under the high-end (SSP585) emission scenario.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Altermagnetic behavior in OsO2: Parallels with RuO2

This dataset contains input and output files from DFT simulations used to reproduce the electronic and phonon band structures of bulk OsO₂ and RuO₂. The files include data from initial electronic structure and phonon calculations performed with Hubbard-U correction (i.e., Antiferromagnetic, AFM) and without Hubbard-U correction (i.e., Non-magnetic, NM). The electronic structure calculations are provided both with and without spin-orbit coupling (SOC). The computed electronic properties are compared with existing literature, while the calculated phonon density of states (PhDOS) is compared with experimental PhDOS.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Lattice structure and dynamics of sparse molecular crystals: OsO4 and RuO4

This dataset contains input and output files from DFT simulations used to reproduce the electronic and phonon calculations of bulk and molecular OsO₄ and RuO₄. The files include data from initial electronic structure and phonon calculations performed using different functionals: PBE, PBEsol, and vdW-DF-optB86b. The computed phonon frequencies are compared with Raman crystal and gas-phase frequencies from existing literature, while the calculated phonon density of states (PhDOS) is compared with experimental PhDOS data.

36 MATERIALS SCIENCE↗

DOE OSTI2019

EGS Collab Experiment 1: TOUGH2-CSM Simulation of Embedded Natural Fractures and Chemical Tracer Transport and Sorption

The EGS Collab SIGMA-V project is a multi-lab and university collaborative research project that is being undertaken at the Sanford Underground Research Facility (SURF) in South Dakota. The project consists of studying stimulation, fluid-flow, and heat transfer processes at a scale of 10-20 m, which is readily amenable to detailed characterization and monitoring. One objective of the project is to establish circulation from injector to producer by hydraulically fracturing the injector. Data generated during these experiments is to be compared with predictions from coupled thermal, hydrological, mechanical, and chemical simulators. One such a simulator, TOUGH2-CSM, has been enhanced in order to simulate EGS Collab SIGMA-V project experiments. These modifications include adding tracers, the capability to model tracer sorption, and an embedded fracture formulation. A set of example problems validate our conservative tracer transport and sorption formulations. We then simulated tracer transport and thermal breakthrough for the first EGS Collab SIGMA-V experiment. This dataset includes the TOUGH2-CSM input and output files associated with the thermal and tracer simulations. A conference paper is included for additional context.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships

Assessing the environmental benefits of proposed flow modification to large rivers provides invaluable insight into future hydropower project operations and relicensing activities. Providing a means to quantitatively define flow-ecology relationships is integral in establishing flow regimes that are mutually beneficial to power production and ecological needs. To compliment this effort an opportunity to create versatile tools that can be applied to broad geographic areas has been presented. In particular, integration with efforts standardized within the ecological limits of hydrologic alteration (ELOHA) is highly advantageous (Poff et al. 2010). This paper presents a geographic information system (GIS) framework for large river classification that houses a base geomorphic classification that is both flexible and accurate, allowing for full integration with other hydrologic models focused on addressing ELOHA efforts. A case study is also provided that integrates publicly available National Hydrography Dataset Plus Version 2 (NHDPlusV2) data, Modular Aquatic Simulation System two-dimensional (MASS2) hydraulic data, and field collected data into the framework to produce a suite of flow-ecology related outputs. The case study objective was to establish areas of optimal juvenile salmonid rearing habitat under varying flow regimes throughout an impounded portion of the lower Snake River, USA as an indicator to determine sites where the potential exists to create additional shallow water habitat. Additionally, an alternative hydrologic classification useable throughout the contiguous United States which can be coupled with the geomorphic aspect of this framework is also presented. This framework provides the user with the ability to integrate hydrologic and ecologic data into the base geomorphic aspect of this framework within a geographic information system (GIS) to output spatiotemporally variable flow-ecology relationship scenarios.

Vernon, Christopher R.↗

DOE OSTI2023

Nuclear data resources and initiatives for nuclear astrophysics

Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the boundaries of what is possible. The latest nuclear results, however, must be specially prepared before they can be used to advance our knowledge of the cosmos. This processing requires a set of resources unique to nuclear astrophysics, and an impressive collection of nuclear reaction and nuclear structure datasets, processing codes, thermonuclear reaction rate libraries, and simulation codes and services have been developed for the field. There are, however, some serious challenges to these efforts that will only worsen in the future, making it important to develop strategies and act now to ensure a sustainable future for this work. After detailing the specific data types needed for nuclear astrophysics and the available data resources, the major challenges in this work and their implications are discussed. A set of initiatives are proposed to meet those challenges along with suggested implementations and possible ways that they may advance our understanding of the Universe and strengthen the field of nuclear astrophysics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (R eco ). However, accurate estimation of ET and R eco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and R eco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and R eco variables, as well as providing reliable estimation of ET and R eco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and R eco and enhances process understanding at sparsely monitored watersheds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

On the Causes and Dynamics of the Early Twentieth Century North American Pluvial

The early twentieth century North American pluvial (1905-1917) was one of the most extreme wet periods of the last five hundred years and directly led to overly generous water allotments in the water-limited American West. Here we examine the causes and dynamics of the pluvial event using a combination of observation-based data sets and general circulation model (GCM) experiments. The character of the moisture surpluses during the pluvial differed by region, alternately driven by increased precipitation (the Southwest), low evaporation from cool temperatures (the Central Plains), or a combination of the two (the Pacific Northwest). Cool temperature anomalies covered much of the west and persisted through most months, part of a globally extensive period of cooler land and sea surface temperatures (SST). Circulation during boreal winter favored increased moisture import and precipitation in the southwest, while other regions and seasons were characterized by near normal or reduced precipitation. Anomalies in the mean circulation, precipitation, and SST fields are partially consistent with the relatively weak El Nino forcing during the pluvial, and also reflect the impact of positive departures in the Arctic Oscillation that occurred in ten of the thirteen pluvial winters. Differences between the reanalysis dataset, an independent statistical drought model, and GCM simulations highlight some of the remaining uncertainties in understanding the full extent of SST forcing of North American hydroclimatic variability.

Cook, Benjamin I.↗

NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗

NASA NTRS2022

An Overview of ARTMIP’s Tier 2 Reanalysis Intercomparison: Uncertainty in the Detection of Atmospheric Rivers and their Associated Precipitation

Atmospheric rivers, or long but narrow regions of enhanced water vapor transport, are an important component of the hydrologic cycle as they are responsible for much of the poleward transport of water vapor and result in precipitation, sometimes extreme in intensity. Despite their importance, much uncertainty remains in the detection of atmospheric rivers in large datasets such as reanalyses and century scale climate simulations. To understand this uncertainty, the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) developed tiered experiments, including the Tier 2 Reanalysis Intercomparison that is presented here. Eleven detection algorithms submitted hourly tags--binary fields indicating the presence or absence of atmospheric rivers--of detected atmospheric rivers in the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts' Reanalysis Version 5 (ERA5) as well as six-hourly tags in the Japanese 55-year Reanalysis (JRA-55). Due to a higher climatological mean for integrated water vapor transport in MERRA-2, atmospheric rivers were detected more frequently relative to the other two reanalyses, particularly in algorithms that use a fixed threshold for water vapor transport. The finer horizontal resolution of ERA5 resulted in narrower atmospheric rivers and an ability to detect atmospheric rivers along resolved coastlines. The fraction of hemispheric area covered by ARs varies throughout the year in all three reanalyses, with different atmospheric river detection tools having different seasonal cycles.

ARTMIP↗

NASA NTRS2022

Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm’s ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies n an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30%, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within +/-20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five 38 used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset -0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40%, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

MODIS↗

NASA NTRSDate not supplied

Detection of CH 4 Hotspots from NASA’s GEOS Composition Analysis System

Recent research indicates that 8 to 12% of the global oil and gas production methane emissions could be attributed to ultra-emitters, which result in high concentration ‘hotspots’ near point sources. Identifying these emissions in near real time provides useful information to the policy makers and private industry, who are working to reduce their impact. To meet this need, scientists are increasingly analyzing satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) instrument aboard ESA’s Sentinel 5-Precursor mission. While direct analysis of TROPOMI level 2 swath data has been successful in identifying some large emission events, identifying hotpots is challenging because of the imaging noise due to a variety of artifacts and limits in daily coverage. Here we explore possible methodologies to detect methane hotspots using a new, gap-filled, and temporally continuous methane product from NASA’s Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), which assimilates column averaged methane mole fractions from the TROPOMI with capabilities to assimilate other remote sensing measurements. The CoDAS has been expanded from a heritage of stratospheric composition and carbon dioxide assimilation allowing for the support of regional modeling, validation with non-coincident operations, and merging variety of datasets. The current work mainly explores Observing System Simulation Experiments with methane GEOS simulations without assimilation to prepare the groundwork for further experiments with assimilated TROPOMI. First, known hotspots based on the known inventory are identified to demonstrate the capability of the system to point out emission hotspots. In the next step, a variety of machine learning techniques such as Self-Organizing Maps and Deep Learning are explored to automate detection of the plumes. Finally, a few approaches to quantify emissions from the identified hotspots are presented and are evaluated against the inventory. The effort is directed toward a future evaluation of the CoDAS based methane monitoring system’s ability to successfully detect and quantify hotspots.

Nikolay Balashov↗

DOE OSTI2013

ParCAT: Parallel Climate Analysis Toolkit

Climate science is employing increasingly complex models and simulations to analyze the past and predict the future of Earth s climate. This growth in complexity is creating a widening gap between the data being produced and the ability to analyze the datasets. Parallel computing tools are necessary to analyze, compare, and interpret the simulation data. The Parallel Climate Analysis Toolkit (ParCAT) provides basic tools to efficiently use parallel computing techniques to make analysis of these datasets manageable. The toolkit provides the ability to compute spatio-temporal means, differences between runs or differences between averages of runs, and histograms of the values in a data set. ParCAT is implemented as a command-line utility written in C. This allows for easy integration in other tools and allows for use in scripts. This also makes it possible to run ParCAT on many platforms from laptops to supercomputers. ParCAT outputs NetCDF files so it is compatible with existing utilities such as Panoply and UV-CDAT. This paper describes ParCAT and presents results from some example runs on the Titan system at ORNL.

Smith, Brian E.↗

DOE OSTI2019

Status Report on Advanced Reactor Fuels Hot Cell Furnace Testing

The Advanced Fuels Campaign is developing a series of out-of-pile steady state furnace tests on irradiated nuclear fuel pins in order to better characterize and understand isolated fuel performance phenomena. These tests will also provide valuable datasets for validation and improvement of modelling and simulation tools. The Blister Anneal Furnace (BAF) in the INL Hot Fuel Examination Facility (HFEF) has been identified as the ideal location for these tests, and, in order to perform them, a new fixture for the BAF has been designed and commissioned. This report summarizes the objectives, design, characteristics and status of this fixture. The design was finalized, materials procured and tested, and the assembly fixture is now fully fabricated in the mock-up shop of the Fuel Conditioning Facility (FCF) at MFC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Datasets for "Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States"

Updated on 2021/07/01 Add python codes for data processing of the NASS yield data, PRISM climate data, RMA insurance data, agmip yield simulation data, and related geospatial analysis-------------------This repository is for the datasets and codes for “Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States” (Li et al. 2019 GCB). The datasets include maize yield, climate data, maize yield loss from crop insurance data in the US, etc. The datasets can reproduce the results of the paper and they can also be reused to explore other topics. Please cite Li et al (2019) or acknowledge this dataset (DOI: 10.6084/m9.figshare.7581473) when using it.

Agricultural spatial analysis and modelling↗