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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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NASA NTRS2014

Uncertainty in Agricultural Impact Assessment

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

damage assessment↗
NASA NTRS2021

Developing a Set of Indicators to Identify, Monitor, and Track Impacts and Change in Forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment(NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

U.S. forests↗
NASA NTRS2013

Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches

Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were 3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EMMM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EMMM comparisons to provide a fuller picture of crop-climate response uncertainties.

DSSAT↗
DOE OSTI2020

Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble

Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950–2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
NASA NTRS2017

Early Action on HFCs Mitigates Future Atmospheric Change

As countries take action to mitigate global warming, both by ratifying the UNFCCC Paris Agreement and enacting the Kigali Amendment to the Montreal Protocol to manage hydrofluorocarbons (HFCs), it is important to consider the relative importance of the pertinent greenhouse gases (GHGs), the distinct structure of their atmospheric impacts, and how the timing of potential GHG regulations would affect future changes in atmospheric temperature and ozone. Chemistry-climate model simulations demonstrate that HFCs could contribute substantially to anthropogenic climate change by the mid-21st century, particularly in the upper troposphere and lower stratosphere i.e., global average warming up to 0.19K at 80hPa. Three HFC mitigation scenarios demonstrate the benefits of taking early action in avoiding future atmospheric change: more than 90 of the climate change impacts of HFCs can be avoided if emissions stop by 2030.

Kigali Amendment↗
NASA NTRS2021

Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Climate risk assessment↗
DOE OSTI2025

Wind and solar energy droughts: Potential impacts on energy system dynamics and research needs

This Perspective article provides a brief overview of the topic of wind and solar energy droughts (henceforth WSDs). It does not attempt to provide a complete literature review of the subject but rather highlights some of the main concepts associated with WSDs. These include wind and solar energy drought definitions and metrics; meteorological conditions producing WSDs; a comparison of their characteristics with hydrologic droughts and hydropower droughts; model-based and observational datasets useful for WSD analyses; the linkage of WSDs to transmission, storage, and demand response; the potential impacts of WSDs vs energy demand variations; wind and solar flood events; WSD predictability; WSD dependency on climate modes of variability; climate change impacts on WSDs; and the special challenge of evaluating the characteristics of WSDs in developing countries that have limited historical data available. Finally, the manuscript identifies research areas that the authors believe would provide immediate benefit to energy system planners.

14 SOLAR ENERGY↗
DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Tree Line Structure and Dynamics at the Northern Limit of the Larch Forest: Anabar Plateau, Siberia, Russia

The goal of the study was to provide an analysis of climate impact before, during, and after the Little Ice Age (LIA) on the larch (Larix gmelinii) tree line at the northern extreme of Siberian forests. Recent decadal climate change impacts on the tree line, regeneration abundance, and age structure were analyzed. The location of the study area was within the forest-tundra ecotone (elevation range 170-450 m) in the Anabar Plateau, northern Siberia. Field studies were conducted along elevational transects. Tree natality/mortality and radial increment were determined based on dendrochronology analyses. Tree morphology, number of living and subfossil trees, regeneration abundance, and age structure were studied. Locations of pre-LIA, LIA, and post-LIA tree lines and refugia boundaries were established. Long-term climate variables and drought index were included in the analysis. It was found that tree mortality from the 16th century through the beginning of the 19th century caused a downward tree line recession. Sparse larch stands experienced deforestation, transforming into tundra with isolated relict trees. The maximum tree mortality and radial growth decrease were observed to have occurred at the beginning of 18th century. Now larch, at its northern boundary in Siberia, is migrating into tundra areas. Upward tree migration was induced by warming in the middle of the 19th century. Refugia played an important role in repopulation of the forest-tundra ecotone by providing a seed source and shelter for recruitment of larch regeneration. Currently this ecotone is being repopulated mainly by tree cohorts that were established after the 1930s. The last two decades of warming did not result in an acceleration of regeneration recruitment because of increased drought conditions. The regeneration line reached (but did not exceed) the pre-LIA tree line location, although contemporary tree heights and stand densities are comparatively lower than in the pre-LIA period. The mean rate of tree line upward migration has been about 0.35 m/yr (with a range of 0.21-0.58), which translates to a tree line response to temperature of about 55 m/deg C.

Northern Limit↗
DOE OSTI2025

A conceptual framework for residential energy security in the context of clean energy transitions

Energy security is a crucial aspect of human well-being. As climate change impacts become more evident, countries are constructing equitable, resilient, and sustainable clean energy transition policies to reduce emissions while ensuring energy security. Climate policies globally highlight the importance of national energy security. Furthermore, adequate and affordable access to household energy is also critical to the continued prioritization of climate mitigation. However, past energy security discussions within the broader climate research and policymaking community primarily focused on national-level energy supply as a critical metric of energy security. Less research has explored the potential implications of energy transitions for residential energy security, often focusing on a single dimension of residential energy security. Thus, we conduct a review of journal articles and governmental plans to develop a conceptual framework of residential energy security and facilitate communication among researchers and policymakers. The framework is designed around four foundational pillars, five metrics measuring residential energy security, and seven drivers influencing the metrics. Additionally, we provide policy examples to show how this framework can be applied to inform decision-making. Thus, this paper makes important contributions to the literature by (a) creating a framework to better understand the concept of energy security at the household level for future research and policy-relevant communications, (b) identifying gaps in the current literature, and (c) highlighting instances where aspects of residential energy security are discussed in policies and governmental plans, which help serve as guiding examples for future applications of our framework in the policymaking processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2017

Selection of a Representative Subset of Global Climate Models that Captures the Profile of Regional Changes for Integrated Climate Impacts Assessment

We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts.

Climate Change; GCMs; Climate Model Subset; Repres↗
DOE OSTI2022

Climate Vulnerability Assessment and Resilience Planning for Idaho National Laboratory

Idaho National Laboratory’s (INL’s) mission is to discover, demonstrate, and secure innovative nuclear energy solutions, other clean energy options, and critical infrastructure. This INL’s Climate Vulnerability Assessment and Resilience Plan (VARP) was developed to enable and sustain that mission while ensuring the viability of operations considering expected climate change impacts. The VARP was developed according to the narrative requirements from the “Vulnerability Assessment and Resilience Planning Guidance, Version 1.2” document issued in February 2022. A prescribed process was used to identify mission-critical systems and components, determine historical and expected climate impacts, and develop resilient solutions. Experts from across INL, including operations staff, researchers, and climate scientists supplied input to the process. Analyses of climate modeling sources revealed that under scenarios of higher and lower greenhouse gas emissions (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5), INL anticipates an increase in climate hazards, including drought, heat waves, wildfire, and precipitation. Increased frequency and duration of climatic hazards forecasts high impacts on certain mission-critical asset and infrastructure types. Utilizing the VARP Risk Assessment Tool, projected high climate hazard impacts across multiple asset and infrastructure types at the INL include energy generation and distribution systems, Site buildings, specialized or mission-critical equipment, and transportation and fleet infrastructure. Some of these mission-critical asset and infrastructure types maintain high adaptive capacity to climatic changes; however, others may need additional adaptive capacity to withstand increased frequency and duration of climate hazards. INL identified close to 300 resilient solutions that were consolidated into 11 solution categories to be tracked in the Department of Energy Sustainability Dashboard. The identified solutions are a starting point for future project development and analysis. These data are intended to inform decision makers on climate issues and potential solutions across INL and associated communities. The VARP is not intended to be a budget tool or project decision document on its own, but rather one of many tools used by decision makers to establish resilient priorities. This initial document provides the framework and foundation to resilient solutions. In the coming years, each solution needs to be fully developed, costed, and prioritized based on mission-critical risk and funding priorities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Climate Scenarios for the NASA / USAID SERVIR Project: Challenges for Multiple Planning Horizons

SERVIR, an acronym meaning "to serve" in Spanish, is a joint venture between NASA and the U.S. Agency for International Development (USAID) which provides satellite-based Earth observation data, modeling, and science applications to help developing nations in Central America, East Africa and the Himalayas improve environmental decision making. Anticipating climate variability / climate change impacts has now become an important component of the SERVIR efforts to build capacity in these regions. Uncertainty in hydrometeorological components of climate variations and exposure to extreme events across scales from weather to climate are of particular concern. We report here on work to construct scenarios or outlooks that are being developed as input drivers for decision support systems (DSSs) in a variety of settings. These DSSs are being developed jointly by a broad array NASA Applied Science Team (AST) Investigations and user communities in the three SERVIR Hub Regions, Central America, East Africa and the Himalayas. Issues span hydrologic / water resources modeling, agricultural productivity, and forest carbon reserves. The scenarios needed for these efforts encompass seasonal forecasts, interannual outlooks, and likely decadal / multi-decadal trends. Providing these scenarios across the different AST efforts enables some level of integration in considering regional responses to climate events. We will discuss a number of challenges in developing this continuum of scenarios including the identification and "mining" of predictability, addressing multiple continental regions, issues of downscaling global model integrations to regional / local applications (i.e. hydrologic and crop modeling). We compare / contrast the role of the U.S. National Multi- Model Experiment initiative in seasonal forecasts and the CMIP-5 climate model experiments in supporting these efforts. Examples of these scenarios, their use, and an assessment of their utility as well as limitations will be presented.

Robertson, Franklin R.↗
NASA NTRS2018

Climate Lecture 20: The Educational Global Climate Model (EdGCM)

Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.

Chandler, Mark A.↗
NASA NTRS2021

Intergenerational Inequities in Exposure to Climate Extremes: Young Generations Are Severely Threatened By Climate Change

Under continued global warming, extreme events such as heat waves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades. Younger generations are therefore expected to face more such events across their lifetimes compared with older generations. This raises important issues of solidarity and fairness across generations that have fueled a surge of climate protests led by young people in recent years and that underpin issues of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming, a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared with another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations (see the first figure), we estimate that children born in 2020 will experience a two- to sevenfold increase in extreme events, particularly heat waves, compared with people born in 1960, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future. Meteorological extremes, hazards, or climate change impacts are mostly studied as they evolve over time under varying emission scenarios and socioeconomic pathways. For example, applying a heat wave indicator (see table S1) to four bias-adjusted global climate models indicates that the land area annually affected by such heat waves will increase from ~15% around 2020 to ~22% by 2100 under a scenario compatible with limiting global warming to 1.5°C, and to ~46% under a scenario in line with current emission reduction pledges (see the first figure). Recent studies extended this approach, studying aspects of climate change as a function of global mean temperature (GMT) increments, highlighting the scenario-independence of several extreme event indicators but remaining, in essence, a comparison of time windows. By contrast, we performed a birth cohort analysis by combining a collection of multimodel extreme event projections with country-scale life expectancy information, gridded population data, and future global temperature trajectories from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C (see supplementary materials). By integrating the exposure of an average person in a country or region to extreme events across their lifetime, we encapsulate spatiotemporal changes in climate hazards, population density, cohort size, and life expectancy (see the first figure).

global warming↗
DOE OSTI2022

Process-based climate change assessment for European winds using EURO-CORDEX and global models

Wind energy is an important pillar of decarbonization strategies and potentially vulnerable to climate change. Existing wind climate change assessments rely on climate models but a systematic investigation of the global-to-regional climate modeling chain is missing. In this study, I highlight key limitations, namely (a) the differing representation of land use change in global and regional climate models which compromises comparability, and (b) the consistency of large-scale features along the global-to-regional climate modeling chain. To this end, I analyze the large European Coordinated Downscaling Experiment (EURO-CORDEX) ensemble (rcp85: N = 49; rcp45: N= 18; rcp26: N = 22) along with the driving global models (rcp85: N = 7; rcp45: N = 5; rcp26: N = 7), finding evidence that climate change reduces mean wind speeds by up to -0.8 ms -1 (offshore) and -0.3 ms -1 (onshore). I provide physical explanations for these changes by identifying two key drivers. First, onshore wind speeds drop in the driving global models in regions and scenarios with strong land use change but show no drop in EURO-CORDEX where land use is held constant. Second, offshore wind reductions follow decreases in the equator-to-pole temperature gradient remarkably well with correlations reaching around 0.9 in resource-rich European countries like Ireland, the United Kingdom and Norway, implying that arctic amplification is a severe risk for European offshore wind energy. My results suggest that earlier conclusions of negligible climate change impacts on wind energy might be premature if either land use changes strongly or polar amplification is at or above the range sampled in global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗