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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Historic climate, cosmogenic 10Be, denudation-rate, and geospatial datasets from the Pikes Peak region, Colorado, USA

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of elevation-dependent denudation rates on Pikes Peak in the Front Range of the Rocky Mountains, Colorado, USA. The package includes GIS layers used to produce the study-area map, including sample locations, sample watershed boundaries, the Pikes Peak batholith, Pleistocene glacier extent, weather station locations, and elevation and hillshade rasters, together with comma-separated value (CSV) tables and matching CSV data dictionaries. These mapped layers provide the geographic framework for interpreting denudation patterns across the Pikes Peak region and for relating sample locations to watershed geometry, bedrock setting, glacial history, and nearby climate stations. The first group of tables reports climate and geospatial context for the study area. These files include station-based temperature and precipitation data used to characterize elevational gradients in mean annual climate and monthly climate seasonality, sample locations, denudation-rate and topographic metrics, fixed frost-cracking model parameters, frost-cracking intensity and precipitation-frequency metrics, and stream-power inversion results. Together, these data provide the basis for evaluating how denudation varies with elevation, climate, and landscape form across sampled catchments on Pikes Peak. The second group of tables reports cosmogenic nuclide and erosion-model results used in the denudation analysis. Included files contain accelerator mass spectrometry (AMS) measurements for in situ-produced cosmogenic beryllium-10 (10Be), including sample identifiers, measured 10Be:9Be ratios, analytical uncertainties, carrier mass, quartz mass, blank corrections, blank-group statistics, and calculated 10Be concentrations and uncertainties. Additional tables summarize stream-power-law inversion results for sampled catchments, including optimized model parameters, predicted erosion rates, residual metrics, channel-pixel counts, and convergence status, as well as regression equations and summary statistics used to evaluate relationships among elevation, climate, frost cracking, precipitation forcing, and denudation rate. The package contains GIS files, comma-separated value files (.csv), Microsoft Excel files (.xlsx), CSV data dictionaries, a file-level metadata table, and a readme text file.

10Be cosmogenic nuclides↗
DOE OSTI2022

A Spatiotemporal-Aware Weighting Scheme for Improving Climate Model Ensemble Predictions

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian neural network (BNN) ensembling method, which combines climate models within a Bayesian model averaging framework, to improve the predictive capability of model ensembles. Our proposed BNN approach calculates spatiotemporally varying model weights and biases by leveraging individual models' simulation skill, calibrates the ensemble prediction against observations by considering observation data uncertainty, and quantifies epistemic uncertainty when extrapolating to new conditions. More importantly, the BNN method provides interpretability about which climate model contributes more to the ensemble prediction at which locations and times. Thus, beyond its predictive capability, the method also brings insights and understanding of the models to guide further model and data development. In this study, we design experiments using an ensemble of CMIP6 climate model simulations to illustrate the BNN ensembling method's capability with respect to prediction accuracy, interpretability, and uncertainty quantification (UQ). We demonstrate that BNN can correctly assign larger weights to the regions and seasons where the individual model fits the observation better. Moreover, its offered interpretability is consistent with our understanding of localized climate model performance. Additionally, BNN shows an increasing uncertainty when the prediction is farther away from the period with constrained data, which appropriately reflects our trustworthiness of the models in the changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

National K-12 Educator Conference; "Earth Then, Earth Now: Our Changing Climate"

With the support of the Department of Energy, the National Science Teachers Association and the National Oceanic and Atmospheric Administration, Imaginary Lines Inc. (dba Sally Ride Science) delivered a highly successful 2-day conference to 165 K-12 educators on climate change. The event took place o July 23rd and 24th, 2008 at the NOAA facility in Silver Spring, MD. The conference celebrated the 25th anniversary of Dr. Sally Ride’s first flight into space in 1983 and examined how our understanding of Earth has changed in those 25 years. One the first day of the conference, participants heard a keynote talk delivered by Dr. Sally Ride, followed by presentations by well-known climate change scientists: Dr. Richard Somerville, Dr. Inez Fung and Dr. Susan Solomon. These sessions were concurrently webcast and made available to educators who were unable to attend the conference. On the second day of the conference, participants attended breakout sessions where they performed climate change activities (e.g. “Neato Albedo!”, “Greenhouse in a Bottle”, “Shell-Shocked”) that they could take back to their classrooms. Additional break-out sessions on using remote sensing images to illustrate climate change effects on Earth’s surface and how to address the climate change debate, were also offered. During lunch, participants attended an Educator Street Fair and had the opportunity to interact with representatives from NOAA, NASA, the EPA, NEEF and the JASON project. A follow-up evaluation survey was administered to all conference attendees immediately following the conference to evaluate its effectiveness. The results of this survey were overwhelmingly positive. The conference materials: presentation Power Points, workshop handouts and activities were available for teachers to download after the conference from the Sally Ride Science website. In summary, the approximately $55K support for the Department of Energy was used to help plan, deliver and evaluate the “Earth Then, Earth Now: Our Changing Climate”, conference which took place on July 23rd and 24th, 2008 at the NOAA facility in Silver Spring, MD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta was responsible for leading the effort to develop statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. We regularly consult with Peter Gleckler (LLNL), Mark Taylor (SNL), and Rich Neale (NCAR) to provide additional insight into issues concerning CAM5 model development and multi-model evaluation of the AR5 archive. This grant was originally assigned to Indiana University where both the PI (Huerta) and the Postdoc (Nosedal) worked on the project from March 1, 2012-March 31, 2013.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗
DOE OSTI2016

Mid-Latitude Circulation and Extremes in a Changing Climate

Mid-latitude extreme weather events are responsible for a large part of climate-related damage. Yet large uncertainties remain in climate model projections of heat waves, droughts, and heavy rain/snow events on regional scales, limiting our ability to effectively use these projections for climate adaptation and mitigation. These uncertainties can be attributed to both the lack of spatial resolution in the models, and to the lack of a dynamical understanding of these extremes. The approach of this project is to relate the fine-scale features to the large scales in current climate simulations, seasonal re-forecasts, and climate change projections in a very wide range of models, including the atmospheric and coupled models of ECMWF over a range of horizontal resolutions (125 to 10 km), aqua-planet configuration of the Model for Prediction Across Scales and High Order Method Modeling Environments (resolutions ranging from 240 km – 7.5 km) with various physics suites, and selected CMIP5 model simulations. The large scale circulation will be quantified both on the basis of the well tested preferred circulation regime approach, and very recently developed measures, the finite amplitude Wave Activity (FAWA) and its spectrum. The fine scale structures related to extremes will be diagnosed following the latest approaches in the literature. The goal is to use the large scale measures as indicators of the probability of occurrence of the finer scale structures, and hence extreme events. These indicators will then be applied to the CMIP5 models and time-slice projections of a future climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The response of soil carbon storage and microbially mediated carbon turnover to simulated climatic disturbance in a northern peatland forest. Revisiting the concept of soil organic matter recalcitrance

The goal of this project was to investigate changes in the structure of dissolved and solid phase organic matter, the production of CO 2 and CH 4 , and the composition of decomposer microbial communities in response to the climatic forcing of environmental processes that determine the balance between carbon gas production versus storage and sequestration in peatlands. Cutting-edge analytical chemistry and next generation sequencing of microbial genes were been applied to habitats at the Marcell Experimental Forest (MEF), where the US DOE’s Oak Ridge National Laboratory and the USDA Forest Service are constructing a large-scale ecosystem study entitled, “Spruce and Peatland Responses Under Climatic and Environmental Change”(SPRUCE). Our study represented a comprehensive characterization of the sources, transformation, and decomposition of organic matter in the S1 bog at MEF. Multiple lines of evidence point to distinct, vertical zones of organic matter transformation: 1) the acrotelm consisting of living mosses, root material, and newly formed litter (0-30 cm), 2) the mesotelm, a mid-depth transition zone (30-75 cm) characterized by labile organic C compounds and intense decomposition, and 3) the underlying catotelm (below 75cm) characterized by refractory organic compounds as well as relatively low decomposition rates. These zones are in part defined by physical changes in hydraulic conductivity and water table depth. O-alkyl-C, which represents the carbohydrate fraction in the peat, was shown to be an excellent proxy for soil decomposition rates. The carbon cycle in deep peat was shown to be fueled by modern carbon sources further indicating that hydrology and surface vegetation play a role in belowground carbon cycling. We provide the first metagenomic study of an ombrotrophic peat bog, with novel insights into microbial specialization and functions in this unique terrestrial ecosystem. Vertical structuring of microbial communities closely paralleled the chemical evolution of peat, with large shifts in microbial populations occurring in the biogeochemical hotspot, the mesotelm, where the highest rates of decomposition were detected. Stable isotope geochemistry and potential rates of methane production paralleled vertical changes in methanogen community composition to indicate a predominance of acetoclastic methanogenesis mediated by the Methanosarcinales in the mesotelm, while hydrogen-utilizing methanogens dominated in the deeper catotelm. Evidence pointed to the availability of phosphorus as well as nitrogen limiting the microbially-mediated turnover of organic carbon at MEF. Prior to initiation of the experimental treatments, our study provided key baseline data for the SPRUCE site on the vertical stratification of peat decomposition, key enzymatic pathways, and microbial taxa containing these pathways. The sensitivity of soil carbon turnover to climate change is strongly linked to recalcitrant carbon stocks and the temperature sensitivity of decomposition is thought to increase with increasing molecular complexity of carbon substrates. This project delivered results on how climate change perturbations impact the microbially-mediated turnover of recalcitrant organic matter in peatland forest soils, both under controlled conditions in the laboratory and at the ecosystem-scale in the field. This project revisited the concept of “recalcitrance” in the regulation of soil carbon turnover using a combination of natural abundance radiocarbon and optical spectroscopic measurements on bulk DOM, and high resolution molecular characterization of DOM. The project elucidated how organic matter reactivity and decomposition will respond to climate change in a both a qualitative (organic matter lability) and quantitiative (increased rates) manner. An Aromaticity Index was developed to represent a more direct and accurate parameter for modeling of DOM reactivity in peatlands. The abundance and community composition of soil microorganisms that mediate C cycling were interrogated with depth in the peat, with season, and in manipulated climate enclosures at unprecedented resolution. Therefore this project delivered strategic new insights on the functioning of peatland ecosystems that collectively store approximately one-third of the world's soil carbon. Furthermore, results from the detailed characterization of DOM lability and microbial community structure/ function will be employed to further develop biogeochemical models to include microbial respiration pathways as well as to track carbon flow with a term that incorporates relative reactivity based on aromaticity index. As it stands now, detailed soil organic matter structure and microbial parameters are not included in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The Second Assessment of the Effects of Climate Change on Federal Hydropower

Hydropower is a key contributor to the US renewable energy portfolio due to its established development history and the diverse benefits it provides to the electric power system. Ensuring the sustainable operation of existing hydropower facilities is of great importance to the US renewable energy portfolio and the reliability of electricity grid. As directed by Congress in Section 9505 of the SECURE Water Act (SWA) of 2009 (Public Law 111-11), the US Department of Energy (DOE), in consultation with the federal Power Marketing Administrations (PMAs) and other federal agencies, has prepared a second quinquennial report on examining the potential effects of climate change on water available for hydropower at federal facilities and on the marketing of power from these federal facilities. This Oak Ridge National Laboratory (ORNL) Technical Memorandum, referred to as the 9505 assessment, describes the technical basis for the report to Congress that was called for in the SWA. To evaluate the potential climate change effects on 132 federal hydropower plants across the entire US, a spatially consistent assessment approach is designed to enable an interregional comparison. This assessment uses a series of models and methods with different spatial resolutions to gradually downscale the global climate change signals into watershed-scale hydrologic projections to support hydropower impact assessment. A variety of historic meteorological and hydrologic observations, hydropower facility characteristics, and geospatial datasets is collected to support model development, calibration, and verification. Among most of the federal hydropower plants throughout the US, the most important climate change effect on hydrology is likely to be the trend toward earlier snowmelt and change of runoff seasonality. Under the projections of increasing winter/spring runoff and decreasing summer/fall runoff, water resource managers may need to consider different water use allocations. With the relatively large storage capacity in the most of the US federal hydropower reservoirs, the system is likely to be able to absorb part of the runoff variability and hence may continue to provide stable annual hydropower generation in the projected near-term and midterm future periods. Nevertheless, the findings are based on the assumption that there is no significant change in the future installed capacity and operation. The issues of aging infrastructures, competing water demand, and environmental requirements may reduce the system s ability to mitigate runoff variability and increase the difficulty of future operation. These issues are not quantitatively analyzed in this study. This study presents a regional assessment at each of the eighteen PMA study areas. This generalized approach allows for spatial consistency throughout all study areas, enabling policymakers to evaluate potential climate change impacts across the entire federal hydropower fleet. This effort is expected to promote better understanding of the sensitivity of federal power plants to water availability and provides a basis for planning future actions that will enable adaptation to climate variability and change.

13 HYDRO ENERGY↗
DOE OSTI2017

Advances in Cross-Cutting Ideas for Computational Climate Science

This report presents results from the DOE-sponsored workshop titled, Advancing X-Cutting Ideas for Computational Climate Science Workshop,'' known as AXICCS, held on September 12--13, 2016 in Rockville, MD. The workshop brought together experts in climate science, computational climate science, computer science, and mathematics to discuss interesting but unsolved science questions regarding climate modeling and simulation, promoted collaboration among the diverse scientists in attendance, and brainstormed about possible tools and capabilities that could be developed to help address them. Emerged from discussions at the workshop were several research opportunities that the group felt could advance climate science significantly. These include (1) process-resolving models to provide insight into important processes and features of interest and inform the development of advanced physical parameterizations, (2) a community effort to develop and provide integrated model credibility, (3) including, organizing, and managing increasingly connected model components that increase model fidelity yet complexity, and (4) treating Earth system models as one interconnected organism without numerical or data based boundaries that limit interactions. The group also identified several cross-cutting advances in mathematics, computer science, and computational science that would be needed to enable one or more of these big ideas. It is critical to address the need for organized, verified, and optimized software, which enables the models to grow and continue to provide solutions in which the community can have confidence. Effectively utilizing the newest computer hardware enables simulation efficiency and the ability to handle output from increasingly complex and detailed models. This will be accomplished through hierarchical multiscale algorithms in tandem with new strategies for data handling, analysis, and storage. These big ideas and cross-cutting technologies for enabling breakthrough climate simulation advancements also need the "glue" of outreach and learning across the scientific domains to be successful. The workshop identified several strategies to allow productive, continuous engagement across those who have a broad knowledge of the various angles of the problem. Specific ideas to foster education and tools to make material progress were discussed. Examples include follow-on cross-cutting meetings that enable unstructured discussions of the types this workshop fostered. A concerted effort to recruit undergraduate and graduate students from all relevant domains and provide them experience, training, and networking across their immediate expertise is needed. This will broaden and expand their exposure to the future needs and solutions, and provide a pipeline of scientists with a diversity of knowledge and know-how. Providing real-world experience with subject matter experts from multiple angles may also motivate the students to attack these problems and even come up with the missing solutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Developing a Climate-Induced Social Vulnerability Index for Urban Areas: A Case Study of East Tennessee

Census American Community Survey 2008-2012 data are used to construct a spatially explicit Climate-Induced Social Vulnerability Index (CSVI) for the East Tennessee area. This CSVI is a combination of a Social Vulnerability Index (SVI) and a Climate Index. A method is replicated and adapted to derive a custom SVI by Census tract for the counties participating in the East Tennessee Index, and a Climate Index is developed for the same area based on indicators for climate hazards. The resulting datasets are exported as a raster to be integrated and combined within the Urban Climate Adaptation Tool (Urban-CAT) to act as an indicator for communities which may be differentially vulnerable to changes in climate. Results for the SVI are mapped separately from the complete CSVI in this document as results for the latter are in development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Atmospheric Research Measurement Climate Research Facility - Atmospheric System Research High-Resolution Modeling Workshop, May 19-20, 2014

The Atmospheric Radiation Measurement (ARM) Climate Research Facility is a scientific user facility within the U.S. Department of Energy (DOE) Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER). ARM is an observation facility whose purpose is to provide ground-based observations of the atmosphere to support climate research and to support the improvement of global climate models. ARM is closely affiliated with the Atmospheric System Research (ASR) program, which uses ARM data to address key atmospheric science issues and improve the parameterization of physical processes in climate models. A common methodology for using ARM data to improve climate models has been to carry out multi-model evaluation projects, typically in conjunction with field campaigns in which intensive radiosonde launches support the development of model forcing data sets. These exercises have yielded important insights. However, in recent years, there has been a growing interest in supporting high-resolution model simulations on a more routine basis. An important aspect of ARM sites is the continuous collection of high-resolution data over long periods of time. More routine model simulations would take better advantage of these long-term data sets. Nevertheless, accomplishing this will require careful attention to the requirements for running and evaluating these models together with a research program that maximizes the benefit of the model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Model-Data Fusion Approaches for Retrospective and Predictive Assessment of the Pan-Arctic Scale Permafrost Carbon Feedback to Global Climate

Policy decisions regarding climate change mitigation and adaptation rely on having reliable projections of future climate change. Coupled climate-carbon models predict that the northern high latitudes will serve as a substantial land carbon sink during the 21stcentury, but these models lack many of the key processes governing high-latitude ecosystem processes. Arctic tundra and boreal forests are unique from other biomes particularly because of the presence of snow, ice and frozen ground, and it is important to understand the mechanisms that drive interconnected processes in these landscapes. Increasing our confidence in climate projections requires an integrated approach where existing, on-going and planned observational and experimental studies of high-latitude ecosystem processes are organized and analyzed in a framework designed to target improvements in understanding key ecosystem-climate feedbacks. For this project we designed an approach to improve our scientific understanding of the permafrost carbon feedback through broad-scale assessment of the drivers and responses of the Arctic system carbon cycle using data-driven models of simple-to-intermediate complexity. The made a valuable contribution to the first of its kind, data-driven quantitative assessment of the impact of permafrost thaw on pan-Arctic scale carbon cycle feedbacks to the global climate system. The approach drew on a conceptual framework for integrating the key system components and their underlying data within a “book-keeping” framework for assessment of the key processes driving the permafrost carbon feedback. The results provided quantitative model-data benchmarking to constrain future projections of carbon cycle responses to a warming Arctic. The framework used environmental proxy data sets examining recent-era geomorphologic land-form transitions and vegetation dynamics, through remotely-sensed data, to guide the extrapolation of site-based data on the key system components to larger regions. These products contribute to the development of finer-scale, more complex representation of permafrost carbon processes in terrestrial biogeochemistry models, to operate within coupled Earth system modeling frameworks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

DOE/UCAR Cooperative Agreement for the Regional and Global Climate Modeling Program

This progress report describes research supported by Cooperative Agreement between the University Corporation for Atmospheric Research (UCAR) and the U.S. Department of Energy (DOE) Biological and Environmental Research (BER). The purpose of the Cooperative Agreement is to provide support for an integrated research program to address key science questions relevant to the DOE Regional and Global Climate Modeling Program as part of the U.S. Global Change Research Program (USGCRP). The Cooperative Agreement funds four tasks that address key aspects of climate science, and this report provides a summary of activities for each task during the past year. As part of the Cooperative Agreement supported science, the Climate Change Prediction (CCP) group at the National Center for Atmospheric Research (NCAR), provides leadership to the Community Earth System Model (CESM) Climate Variability and Change Working Group (CVCWG) through co-chairs Gerald Meehl and Susan Bates (with DOE-funded co-chair Peter Gleckler, along with Clara Deser and Shang-Ping Xie). Over the past year CCP has played a leadership role in performing climate change simulations and analyses with the Community Climate System Model version 4 (CCSM4) and CESM version 1. These simulations and analyses made strong contributions to the federal USGCRP in several high priority areas. We have also been involved in planning for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) which started in 2015. Simulations from CMIP6 will form the basis for ongoing U.S. National Assessment activities, and the IPCC AR6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Potential Impacts of Accelerated Climate Change (4th Annual Report)

Pacific Northwest National Laboratory (PNNL) has completed the fourth year draft Annual Letter Report that summarizes i) NRC needs for climate and hydrologic information, ii) recent research into climate and hydrologic changes that may affect nuclear power plants; iii) recent reports by federal agencies, as well as national and international organizations tasked with climate change research and developing policy with respect to climate change and mitigation; and iv) trends and advances in accommodating required climate change estimates in site specific evaluations. The Northeast U.S., a region defined by the Forth National Climate Assessment is the focus of this report. The NRC COR will review and provide comments on this draft within four weeks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling

Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate Interventions Risk Framework White Paper

This work describes a structured, risk-based approach for analyzing questions related to the potential deployment of a climate intervention in any user-established future scenario, and further provides a structured means for considering what the potential consequences of such interventions may be. Because the climatological, technological, and geopolitical conditions are currently not believed to be supportive of climate intervention use, the framework establishes a three-pronged approach to frame key analytical questions predicated on future scenarios of interest to the user. These three pieces are: (1) Building a scenario. (2) Assessing the likelihood of that scenario based on assumptions about an actor and their motivations for deploying a climate intervention. (3) Analyzing the potential consequences of that climate intervention. Finally, results from this consequence analysis are compared against projected consequences of unabated climate change as a complete risk analysis of deploying a climate intervention.

54 ENVIRONMENTAL SCIENCES↗