The Arctic in E3SM: Progress, Opportunities, and Challenges
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Arctic↗
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Results for “Earth system science”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Basin-scale investigations of topographic influence on permafrost thaw in ice wedge-affected landscapes
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Earth Sciences↗
Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model
A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.
54 ENVIRONMENTAL SCIENCES↗
Research Development and Partnership Pilot (RDPP): Developing plans and partnerships for incorporating tree reproduction to understand Earth system change
The goals for this Research Development and Partnership Pilot (RDPP) proposal were to i) learn about Department of Energy (DOE) research and use the PI’s expertise on patterns and environmental drivers of tree reproduction as a basis to make connections with individuals and research groups at DOE National Laboratories, and ii) to develop plans and form partnerships that will both enhance the PI’s research. The objectives of the proj ect were for the PI to i) participate in the Department of Energy's Office of Science program in Biological and Environmental Research training and outreach activities, ii) conduct directed fact-finding on Earth and Environmental Systems Sciences Division (EESSD) research projects and new partnerships with individuals and groups at National Laboratories, ii) conduct meetings with potential collaborators at National Laboratories to discuss EESSD-relevant research ideas, and iv) to develop a plan for future research efforts. During the period of the award, the objectives of the proposal were met, as the PI attended Environmental System Sciences meetings, American Geophysical Union meetings, SPRUCE experiment meetings, and visited the Oak Ridge National Lab. These activities led to increased understanding of the research being conducted by DOE scientists and the PI had meetings with potential collaborators to develop future research plans that leverage expertise at DOE and the research interests of the PI to increase understanding of the role of tree reproduction in future carbon allocation and in tree regeneration models in boreal ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Effect of disorder on thermodynamic instability of binary Rare-earth – Nickel – Palladium compounds
In this work, we have investigated the thermodynamic stability of disordered rare-earth phases SmX 2 and Sm 10 X 21 (X=Ni, Pd) using machine-learning based analytical descriptor and first-principles density functional theory methods. The absence of Laves phase compounds in R-Pd binary systems is a longstanding problem of rare earth science: even though Ni and Pd belong to the same group of the periodic table and have similar electronic structure, the Pd compound crystallizes in a monoclinic (C2/m) phase with 10:21 stoichiometry, i.e., Sm 10 Pd 21 , while the Ni compound adopts a cubic Laves phase (MgCu 2 ) structure. To understand this contrasting phase stability, we performed thermodynamic convex hull analysis of Sm x Ni 1-x and Sm x Pd 1-x binary systems, which is experimentally validated using powder X-ray diffraction (PXRD) analyzes of polycrystalline Sm(Ni x Pd 1-x ) 2 samples with x=0, 0.5, and 1. A detailed electronic-structure (band-structure, charge density, and Fermi-surface) analysis of the differences between SmNi 2 /SmPd 2 and Sm 10 Ni 21 /Sm 10 Pd 21 compounds provides the quantum mechanical origin of the unfavorable mixing of Pd with Ni in cubic Laves phase. We show that the stability of Sm-Pd in 10:21 stoichiometry arises from improved intra-/inter-layer 5d-4d bonding compared to the 1:2 stoichiometry. Our work emphasizes the importance of ab-initio methods and computationally inexpensive analytical descriptors for the detailed analysis of thermodynamic and electronic properties of hard-to-prepare rare-earth compounds.
36 MATERIALS SCIENCE↗
Addressing Uncertainty in MultiSector Dynamics Research
MultiSector Dynamics (MSD) research explores the dynamics and co-evolutionary pathways of human and Earth systems and the emerging interdependent sectors of energy, water, agriculture, and transportation among others. The interactions between these sectors are central to MSD science, as they capture how processes and feedbacks across Earth, environmental, infrastructure, and societal systems shape transitions, socioeconomic risks, and the provision of services. By definition, MSD research requires deep integration across diverse scientific disciplines, ranging from the natural to the social sciences and engineering. All these disciplines apply a variety of numerical simulation models to study and understand their underlying systems of focus. The utility of these models hinges on the fidelity with which they represent the real systems and their ability to produce novel insights about systems and their interactions. The coupled human-natural systems typically represented are shaped by a multitude of interdependent human and natural processes which, when modeled, translate to highly complex, non-linear, interacting behaviors. This ever-increasing complexity massively expands the uncertainty space of a model and can be found in model inputs, processes and parameters. This is further amplified when several models are combined to answer multisectoral questions, as additional uncertainty regarding coupling relationships and interactions is introduced.
IM3, Uncertainty and Sensitivity Analysis, multise↗
Alignment among environmental programs in higher education: What Food-Energy-Water Nexus concepts are covered in introductory courses?
Interdisciplinary environmental and sustainability (IES) programs are different from other fields because they focus on a complex integration of humanities, social, and natural sciences concepts centered on the interactions of coupled human and natural systems. The interdisciplinary nature of IES programs does not lend itself to traditional discipline-specific concept inventory frameworks for critically evaluating preconceptions and learning. We discuss the results of the first phase of a research project to develop a next generation concept inventory for evaluating interdisciplinary concepts important for introductory IES courses. Using the Food-Energy-Water (FEW) Nexus (the intersections/interdependencies of food, energy, and water sectors) as our focus, we conducted a content analysis of eight representative college-level introductory environmental course syllabi and course materials (e.g., textbooks, journal articles, print media) to identify common interdisciplinary FEW Nexus concepts taught in introductory IES courses. Results demonstrate that all IES introductory course materials reference the FEW Nexus. Food, energy, and/or water resources as individual elements of the FEW Nexus are frequently described, but connections between these resource systems are included less often. Biology, energy systems, waste and pollution in the natural environment, agriculture, earth sciences and geology, climate change, behavioral social sciences, and economics concepts are most associated with FEW concepts, hinting at commonalities across IES topics that anchor systems thinking. Despite differences in IES programs, there appears to be some alignment between core concepts being taught at the FEW Nexus in introductory courses.
54 ENVIRONMENTAL SCIENCES↗
Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections
Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4
Climate Change↗
The origin of volatile elements in the Earth–Moon system
Significance Understanding the history of volatile species such as water in the Earth–Moon system is a major objective of planetary science. In this work, we use the moderately volatile element Rb, which has a long-lived isotope ( 87 Rb) that decays to 87 Sr, to show that lunar volatile element depletion was not caused by the Moon-forming impact. The Rb–Sr systematics of lunar rocks mandate that the bodies involved in the impact that formed the Earth–Moon system were depleted in volatile elements relative to the bulk solar system prior to the impact. As such, Earth’s relatively small proportion of water is either primarily indigenous or was added after the Giant Impact from a source that contained essentially no moderately volatile elements.
58 GEOSCIENCES↗
Framework for Antarctic System Science in E3SM Overview
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Earth Sciences↗
A review of coarse mineral dust in the Earth system
Mineral dust particles suspended in the atmosphere span more than three orders of magnitude in diameter, from <0.1 µm to more than 100 µm. This wide size range makes dust a unique aerosol species with the ability to interact with many aspects of the Earth system, including radiation, clouds, hydrology, atmospheric chemistry, and biogeochemistry. This review focuses on coarse and super-coarse dust aerosols, which we respectively define as dust particles with a diameter of 2.5–10 µm and 10–62.5 µm. We review several lines of observational evidence indicating that coarse and super-coarse dust particles are transported farther than previously expected and that the abundance of these particles is substantially underestimated in current global models. We synthesize previous studies that used observations, theories, and model simulations to highlight the impacts of coarse and super-coarse dust aerosols on the Earth system, including their effects on dust-radiation interactions, dust-cloud interactions, atmospheric chemistry, and biogeochemistry. Specifically, coarse and super-coarse dust aerosols produce a net positive direct radiative effect (warming) at the top of the atmosphere and can modify temperature and water vapor profiles, influencing the distribution of clouds and precipitation. In addition, coarse and super-coarse dust aerosols contribute a substantial fraction of ice-nucleating particles, especially at temperatures above –23 °C. They also contribute a substantial fraction to the available reactive surfaces for atmospheric processing and the dust deposition flux that impacts land and ocean biogeochemistry by supplying important nutrients such as iron and phosphorus. Furthermore, we examine several limitations in the representation of coarse and super-coarse dust aerosols in current model simulations and remote-sensing retrievals. Because these limitations substantially contribute to the uncertainties in simulating the abundance and impacts of coarse and super-coarse dust aerosols, we offer some recommendations to facilitate future studies. Overall, we conclude that an accurate representation of coarse and super-coarse properties is critical in understanding the impacts of dust aerosols on the Earth system.
54 ENVIRONMENTAL SCIENCES↗
Sample Identifiers and Metadata Reporting Format for Environmental Systems Science
The ESS-DIVE sample identifiers and metadata reporting format primarily follows the System for Earth Sample Registration (SESAR) Global Sample Number (IGSN) guide and template, with modifications to address Environmental Systems Science (ESS) sample needs and practicalities (IGSN-ESS). IGSNs are associated with standardized metadata to characterize a variety of different sample types (e.g. object type, material) and describe sample collection details (e.g. latitude, longitude, environmental context, date, collection method). Globally unique sample identifiers, particularly IGSNs, facilitate sample discovery, tracking, and reuse; they are especially useful when sample data is shared with collaborators, sent to different laboratories or user facilities for analyses, or distributed in different data files, datasets, and/or publications. To develop recommendations for multidisciplinary ecosystem and environmental sciences, we first conducted research on related sample standards and templates. We provide a comparison of existing sample reporting conventions, which includes mapping metadata elements across existing standards and Environment Ontology (ENVO) terms for sample object types and environmental materials. We worked with eight U.S. Department of Energy (DOE) funded projects, including those from Terrestrial Ecosystem Science and Subsurface Biogeochemical Research Scientific Focus Areas. Project scientists tested the process of registering samples for IGSNs and associated metadata in workflows for multidisciplinary ecosystem sciences. We provide modified IGSN metadata guidelines to account for needs of a variety of related biological and environmental samples. While generally following the IGSN core descriptive metadata schema, we provide recommendations for extending sample type terms, and connecting to related templates geared towards biodiversity (Darwin Core) and genomic (Minimum Information about any Sequence, MIxS) samples and specimens. ESS-DIVE recommends registering samples for IGSNs through SESAR, and we include instructions for registration using the IGSN-ESS guidelines. Our resulting sample reporting guidelines, template (IGSN-ESS), and identifier approach can be used by any researcher with sample data for ecosystem sciences.
54 ENVIRONMENTAL SCIENCES↗
Vegetation type is an important predictor of the arctic summer land surface energy budget
Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm –2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.
54 ENVIRONMENTAL SCIENCES↗
Exploring Multiscale Earth System and Human-Earth System Dynamics in the Puget Sound Region
With its mountain-to-coast hydroclimate, strong influence of Pacific Ocean weather systems and climate patterns, and unique land use history with strong rural-to-urban gradients, the Puget Sound region is a natural laboratory for studying a number of complex processes in, and interactions among, different Earth and human systems. A 1-year scoping study was initiated by the Earth and Environmental Systems Modeling program of the Department of Energy’s Office of Science Biological and Environmental Research. It was intended to elucidate and highlight the rich opportunities Puget Sound offers to advance our understanding of and ability to simulate Earth system changes and human-Earth system interactions. A literature review, multi-day community workshop, and external input were used to develop this scoping study report. The report first summarizes scientific understanding and knowledge gaps associated with major regional systems, including atmosphere and climate, the land surface, coastal and marine processes, and human systems, as well as how these systems are changing over time. It then highlights some of the most notable extreme events in the region, including heat waves, atmospheric rivers, droughts, and wildfires. Finally, key research opportunities for Earth and environmental systems modeling in, above, and around Puget Sound are highlighted.
54 ENVIRONMENTAL SCIENCES↗
Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'
This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).
54 ENVIRONMENTAL SCIENCES↗
Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential
Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.
54 ENVIRONMENTAL SCIENCES↗
Effects of Rapid Permafrost Thaw on CO2 and CH4 Fluxes in a Warmer and Wetter Future (Final Technical Report)
When ice-rich permafrost thaws, the ground subsides, creating thermokarst landscapes with dramatically different soil conditions and carbon fluxes than the original ecosystem. Roughly 20% of the northern permafrost region is susceptible to thermokarst formation (Olefeldt et al., 2016). Thermokarst formation is often rapid; tens of meters of permafrost can thaw within a few years (Schuur et al., 2015). In topographically low areas, thermokarst thaw converts boreal forest or tundra dry shrub ecosystems into sedge or Sphagnum moss wetlands (Olefeldt et al., 2016). While this type of landscape transformation releases carbon stored in permafrost into the atmosphere, on longer time scales, it facilitates sequestration of atmospheric carbon in plant biomass because permafrost thaw releases plant-available nutrients and wetlands are highly productive (M. C. Jones et al., 2017). However, wetlands also generate methane, which is a potent greenhouse gas. Methane emissions from thermokarst wetlands can cause these carbon-sequestering systems to have a positive global warming potential (Johansson et al., 2006; Turetsky et al., 2007). Our project objective was to improve Earth System and environmental predictability by advancing understanding of how CO 2 and CH 4 flux in permafrost thaw-induced wetlands (thermokarst) will change in the future as temperatures and climate conditions shift. Northern latitudes are expected to get warmer and wetter (IPCC 2013), and initiation and expansion of thermokarst thaw is expected to increase (Jorgenson et al. 2006; Zhang et al. 2017). Given these expected changes, our work sought to address three broad questions: Q1) How will northern latitude CO2 and CH4 emissions respond to warming temperatures? Q2) What is the impact of precipitation on permafrost thaw and carbon emissions? Q3) How do CO2 and CH4 emissions change as wetlands age after permafrost thaw? To answer these questions, we took both a modeling and measurement approach. Modeling work was conducted with DOE’s Energy Exascale Earth System Model (E3SM) land model (ELM). Empirical work took place in two primary locations. The first was a thermokarst site near Fairbanks, AK. The site is part of the Bonanza Creek Long Term Ecological Research program and is well instrumented (Neumann et al., 2019). The second was an isolated thawing permafrost wetland located on Kenai Peninsula — Brown’s Lake bog (B. M. Jones et al., 2016) — where the current climate is representative of what is expected at higher latitudes in the future. This site provides an ideal opportunity to test our hypotheses about how thermokarst wetland dynamics will respond to future environmental conditions. In addition, the project collaborated with researchers asking similar questions who were collecting measurements in high latitude post-glacial lakes located in collaborators tackling similar questions in Sweden (Stordalen Mire) (Emerson et al., 2021). Project efforts directly aligned with the stated goal of the funding opportunity announcement (FOA), which was “to improve the understanding and representation of terrestrial ecosystems in ways that advance Earth system model parameterizations and capabilities... thereby improving the quality of Earth and environmental model projections and providing the scientific foundation needed to support DOE’s science and energy missions.” Specifically, the project improved sophistication and accuracy of the Energy Exascale Earth System Model (E3SM), which is being developed primarily at DOE National Laboratories to support scientific research and decision-making.
54 ENVIRONMENTAL SCIENCES↗
Automation is all you need: Faster Earth system models with AI/ML
Focal Area: Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI) and advanced methods. Science Challenge: Tropical cyclones can in- duce extreme water cycle events through dramatic precipitation and storm surge. More reliable models of intensity will translate into better prediction of the impact of extreme events in large scale Earth systems simulations. We demonstrate and describe AI/ML methodologies for rapid assimilation of new, in situ data products.
54 ENVIRONMENTAL SCIENCES↗