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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Climate Change Impacts on Natural Sulfur Production: Ocean Acidification and Community Shifts

Utilizing the reduced-complexity model Hector, a regional scale analysis was conducted quantifying the possible effects climate change may have on dimethyl sulfide (DMS) emissions within the oceans. The investigation began with a review of the sulfur cycle in modern Earth system models. We then expanded the biogeochemical representation within Hector to include a natural ocean component while accounting for acidification and planktonic community shifts. The report presents results from both a latitudinal and a global perspective. This new approach highlights disparate outcomes which have been inadequately characterized via planetary averages in past publications. Our findings suggest that natural sulfur emissions (ESN) may exert a forcing up to 4 times that of the CO 2 marine feedback, 0.62 and 0.15 Wm –2 , respectively, and reverse the radiative forcing sign in low latitudes. Additionally, sensitivity tests were conducted to demonstrate the need for further examination of the DMS loop. As a result, the present work attempts to include dynamic ESN within reduced-complexity simulations of the sulfur cycle, illustrating its impact on the global radiative budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Multifactor Experiment and Model Integration to Determine the Regional Vulnerability of Permafrost Carbon to Climate Change. Final report

The major research goal of this project was to understand and quantify the fate of carbon stored in permafrost ecosystems using a combination of field and laboratory experiments to measure and model isotope ratios and carbon fluxes in a tundra ecosystem exposed to experimental warming. Field measurements centered on a two-factor experimental warming to increase air and soil temperatures alone, and in combination, at a tundra field site in Alaska. A second manipulation of water table was embedded within the warming treatment such that both major environmental factors (temperature, moisture) controlling ecosystem carbon dynamics were experimentally altered. The experiment was interfaced with modeling activities using both data assimilation and forward modeling approaches. Models were used to make forecasts of ecosystem carbon dynamics beyond the time frame and environmental space of the experiment itself. These results were linked with ongoing synthesis and model intercomparison activities through the Permafrost Carbon Network, which led to additional synthesis publications as a result of collaboration and data sharing from this project. As key outcomes, this project has produced new papers published in the peer-reviewed literature, archived datasets used for model-data intercomparisons, trained graduate students and postdoctoral researchers, and has raised awareness about the vulnerability of permafrost carbon to a wider audience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Diagnostic indicators for integrated assessment models of climate policy

Integrated assessments of how climate policy interacts with energy-economic systems can be performed by a variety of models with different functional structures. This article proposes a diagnostic scheme that can be applied to a wide range of integrated assessment models to classify differences among models based on their carbon price responses. Model diagnostics can uncover patterns and provide insights into why, under a given scenario, certain types of models behave in observed ways. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study with 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to more easily explain variations among policy-relevant model results.

Climate change economics↗
DOE OSTI2016

A multi-model assessment of the co-benefits of climate mitigation for global air quality

The recent International Panel on Climate change (IPCC) report identifies significant co-benefits from climate policies on near-term ambient air pollution and related human health outcomes [1]. This is increasingly relevant for policy making as the health impacts of air pollution are a major global concern- the Global Burden of Disease (GBD) study identifies outdoor air pollution as the sixth major cause of death globally [2]. Integrated assessment models (IAMs) are an effective tool to evaluate future air pollution outcomes across a wide range of assumptions on socio-economic development and policy regimes. The Representative Concentration Pathways (RCPs) [3] were the first set of long-term global scenarios developed across multiple integrated assessment models that provided detailed estimates of a number of air pollutants until 2100. However these scenarios were primarily designed to cover a defined range of radiative forcing outcomes and thus did not specifically focus on the interactions of long-term climate goals on near-term air pollution impacts. More recently, [4] used the RCP4.5 scenario to evaluate the co-benefits of global GHG reductions on air quality and human health in 2030. [5-7] have further examined the interactions of more diverse pollution control regimes with climate policies. This paper extends the listed studies in a number of ways. Firstly it uses multiple IAMs to look into the co-benefits of a global climate policy for ambient air pollution under harmonized assumptions on near-term air pollution control. Multi-model frameworks have been extensively used in the analysis of climate change mitigation pathways, and the structural uncertainties regarding the underlying mechanisms (see for example [8-10]. This is to our knowledge the first time that a multi-model evaluation has been specifically designed and applied to analyze the co-benefits of climate change policy on ambient air quality, thus enabling a better understanding of at a detailed sector and region level. A second methodological advancement is a quantification of the co-benefits in terms of the associated atmospheric concentrations of fine particulate matter (PM2.5) and consequent mortality related outcomes across different models. This is made possible by the use of state-of the art simplified atmospheric model that allows for the first time a computationally feasible multi-model evaluation of such outcomes.

Rao, Shilpa↗
NASA NTRS2006

Introduction to MODIS Cloud Products

The Earth's radiative energy balance and hydrological cycle are fundamentally coupled with the distribution and properties of clouds. Therefore, the ability to remotely infer cloud properties and their variation in space and time is crucial for establishing climatologies as a reference for validation of present-day climate models and in assessing future climate change. Remote cloud observations also provide data sets useful for testing and improving cloud model physics, and for assimilation into numerical weather prediction models. The MODerate Resolution Imaging Spectroradiometer (MODIS) imagers on the Terra and Aqua Earth Observing System (EOS) platforms provide the capability for globally retrieving these properties using passive solar reflectance and infrared techniques. In addition to providing measurements similar to those offered on a suite of historical operational weather platforms such as the Advanced Very High Resolution Radiometer (AVHRR), the High-resolution Infrared Radiation Sounder (HIRS), and the Geostationary Operational Environmental Satellite (GOES), MODIS provides additional spectral and/or spatial resolution in key atmospheric bands, along with on-board calibration, to expand the capability for global cloud property retrievals. The core MODIS operational cloud products include cloud top pressure, thermodynamic phase, optical thickness, particle size, and water path, and are derived globally at spatial resolutions of either 1- or 5-km (referred to as Level-2 or pixel-level products). In addition, the MODIS atmosphere team (collectively providing cloud, aerosol, and clear sky products) produces a combined gridded product (referred to as Level-3) aggregated to a 1 equal-angle grid, available for daily, eight-day, and monthly time periods. The wealth of information available from these products provides critical information for climate studies as well as the continuation and improved understanding of existing satellite-based cloud climatologies derived from heritage instruments. This chapter provides an overview of the MODIS Level-2 and -3 operational cloud products.

Baum, Bryan A.↗
NASA NTRS1984

The influence of ocean surface temperature gradient and continentality on the Walker circulation. I - Prescribed tropical changes

A coarse mesh global climate model has been developed to assess ocean surface temperature (OST) gradient and continentality influences on the Walker circulation, which is characterized in the zonal plane by three pairs of clockwise and counterclockwise cells in the troposphere. The model response exhibits statistically significant changes in the intensity of the various cells and branches with small shifts in the east-west extent. The overall structure in the zonal plane for experiments with the coldest and with mean temperatures, however, remained unchanged. In an experiment involving the replacement of the South American continent by an ocean with OSTs linearly interpolated from the eastern Pacific to the western Atlantic, a dramatic change took place in the structure of the Walker circulation. It is concluded that both continentality and OST gradient are important Walker circulation forcing mechanisms.

Chervin, R. M.↗
DOE OSTI2018

Dynamic, Intermediate Soil Carbon Pools May Drive Future Responsiveness to Environmental Change

Accurately capturing dynamic soil response to disturbance effects in agroecosystem models remains elusive, thereby limiting projections of climate change mitigation potential. Perennial grasses cultivated in zero‐tillage management systems hold promise as sustainable agroecosystems. High‐yielding tropical C 4 grasses often have extensive rooting systems, and the belowground processes of root turnover, aggregate formation, and mineral stabilization drove rapid C accumulation after cultivation in a recent study. We sought (i) to understand and constrain the size and responsiveness of dynamic, intermediate‐cycling C pools contributing to the observed C accrual rates, and (ii) to simulate C stocks over time under the disturbance of elevated temperature using soil incubation at multiple temperatures and physical fractionation via density and sonication. Three‐pool transfer modeling of soil incubations revealed small pools of readily available (i.e., days to months) microbial substrate that were responsive to temperature, time since cultivation, and inputs. Larger, kinetically slow‐cycling pools were more indicative of long‐term (i.e., years to decades) changes in C stock and strongly connected to measured changes in physical fractions. Combining the sensitivity of readily available microbial substrate with three‐pool transfer modeling of the physical fractions over time since cultivation revealed that dynamic transfers of inputs occurred between the free organic and aggregate‐protected fractions, and from these fractions to the mineral‐associated dense fraction. Under 5°C temperature elevation, increased transfer rates outweighed elevated decomposition losses to sustain soil C accrual into the future. To effectively plan managed landscapes and monitor sustainable agroecosystems for climate change mitigation, tools must incorporate the complexity of soil response to change. Core Ideas Dynamic aspects of soil C are inadequately represented in most models. Intermediate‐cycling pools are more responsive to disturbances than thought. Root inputs are rapidly moved from particulate debris to mineral‐bound stable pools. Temperature increased the transfer rate to stable pools and sustained C accumulation Climate change mitigation projections must capture dynamic responses in agroecosystems.

Crow, Susan E.↗
DOE OSTI2020

Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation

Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems. Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint. Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality. These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections. Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation. Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of “seasonally variable regimes becoming more variable”. Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Projections of Rapidly Rising Temperatures over Africa Under Low Mitigation

An analysis of observed trends in African annual-average near-surface temperatures over the last five decades reveals drastic increases, particularly over parts of the subtropics and central tropical Africa. Over these regions, temperatures have been rising at more than twice the global rate of temperature increase. An ensemble of high-resolution downscalings, obtained using a single regional climate model forced with the sea-surface temperatures and sea-ice fields of an ensemble of global circulation model (GCM) simulations, is shown to realistically represent the relatively strong temperature increases observed in subtropical southern and northern Africa. The amplitudes of warming are generally underestimated, however. Further warming is projected to occur during the 21st century, with plausible increases of 4-6 C over the subtropics and 3-5 C over the tropics by the end of the century relative to present-day climate under the A2 (a low mitigation) scenario of the Special Report on Emission Scenarios. High impact climate events such as heat-wave days and high fire-danger days are consistently projected to increase drastically in their frequency of occurrence. General decreases in soil-moisture availability are projected, even for regions where increases in rainfall are plausible, due to enhanced levels of evaporation. The regional downscalings presented here, and recent GCM projections obtained for Africa, indicate that African annual-averaged temperatures may plausibly rise at about 1.5 times the global rate of temperature increase in the subtropics, and at a somewhat lower rate in the tropics. These projected increases although drastic, may be conservative given the model underestimations of observed temperature trends. The relatively strong rate of warming over Africa, in combination with the associated increases in extreme temperature events, may be key factors to consider when interpreting the suitability of global mitigation targets in terms of African climate change and climate change adaptation in Africa.

climate change↗
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
DOE OSTI2025

Data for Bistline, et al. (2025) "Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts"

These files contain input assumptions, results, and figures associated with the Bistline, et al. (2025) article "Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts" in Energy and Climate Change as part of the Energy Modeling Forum 37 study. Please refer to the original paper for details.

climate policy↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Expansion of Coccidioidomycosis Endemic Regions in the United States in Response to Climate Change

Coccidioidomycosis (Valley fever) is a fungal disease endemic to the southwestern United States. Across this region, temperature and precipitation influence the extent of the endemic region and number of Valley fever cases. Climate projections for the western United States indicate that temperatures will increase and precipitation patterns will shift, which may alter disease dynamics. We estimated the area potentially endemic to Valley fever using a climate niche model derived from contemporary climate and disease incidence data. We then used our model with projections of climate from Earth system models to assess how endemic areas will change during the 21st century. By 2100 in a high warming scenario, our model predicts that the area of climate-limited endemicity will more than double, the number of affected states will increase from 12 to 17, and the number of Valley fever cases will increase by 50%. The Valley fever endemic region will expand north into dry western states, including Idaho, Wyoming, Montana, Nebraska, South Dakota, and North Dakota. Precipitation will limit the disease from spreading into states farther east and along the central and northern Pacific coast. This is the first quantitative estimate of how climate change may influence Valley fever in the United States. Our predictive model of Valley fever endemicity may provide guidance to public health officials to establish disease surveillance programs and design mitigation efforts to limit the impacts of this disease.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

An LCA model for waste incineration enhanced with new technologies for metal recovery and application to the case of Switzerland

Highlights: • An enhanced process-based LCA model for MSWI is featured and applied in case study. • LCA modeling of recent technological developments for metal recovery from fly ash. • Net release from Swiss MSWI 133 kg CO{sub 2}-eq/tonne waste from attributional LCA perspective. • Net savings from a consequential LCA perspective reach up to 303 kg CO{sub 2}-eq/tonne waste. • Impacts according to ReCiPe and CExD show similar pattern to climate change. - Abstract: A process model of municipal solid waste incinerators (MSWIs) and new technologies for metal recovery from combustion residues was developed. The environmental impact is modeled as a function of waste composition as well as waste treatment and material recovery technologies. The model includes combustion with a grate incinerator, several flue gas treatment technologies, electricity and steam production from waste heat recovery, metal recovery from slag and fly ash, and landfilling of residues and can be tailored to specific plants and sites (software tools can be downloaded free of charge). Application of the model to Switzerland shows that the treatment of one tonne of municipal solid waste results on average in 425 kg CO{sub 2}-eq. generated in the incineration process, and 54 kg CO{sub 2}-eq. accrue in upstream processes such as waste transport and the production of operating materials. Downstream processes, i.e. residue disposal, generates 5 kg CO{sub 2}-eq. Savings from energy recovery are in the range of 67 to 752 kg CO{sub 2}-eq. depending on the assumptions regarding the substituted energy production, while the recovery of metals from slag and fly ash currently results in a net saving of approximately 35 kg CO{sub 2}-eq. A similar impact pattern is observed when assessing the MSWI model for aggregated environmental impacts (ReCiPe) and for non-renewable resource consumption (cumulative exergy demand), except that direct emissions have less and no relevance, respectively, on the total score. The study illustrates that MSWI plants can be an important element of industrial ecology as they provide waste disposal services and can help to close material and energetic cycles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Matrix Approach to Land Carbon Cycle Modeling

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin-up of land carbon cycle models by tens of times. The accelerated spin-up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever-increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Seed origin and warming constrain lodgepole pine recruitment, slowing the pace of population range shifts

Understanding how climate warming will affect the demographic rates of different ecotypes is critical to predicting shifts in species distributions. In this study, we present results from a common garden, climate change experiment in which we measured seedling recruitment of lodgepole pine, a widespread North American conifer that is also planted globally. Seeds from a low-elevation provenance had more than three-fold greater recruitment to their third year than seeds from a high-elevation provenance across sites within and above its native elevation range and across climate manipulations. Heating halved recruitment to the third year of both low- and high-elevation seed sources across the elevation gradient, while watering more than doubled recruitment, alleviating some of the negative effects of heating. Demographic models based on recruitment data from the climate manipulations and long-term observations of adult populations revealed that heating could effectively halt modeled upslope range expansion except when combined with watering. Simulating fire and rapid postfire forest recovery at lower elevations accelerated lodgepole pine expansion into the alpine, but did not alter final abundance rankings among climate scenarios. Regardless of climate scenario, greater recruitment of low-elevation seeds compensated for longer dispersal distances to treeline, assuming colonization was allowed to proceed over multiple centuries. In conclusion, our results show that ecotypes from lower elevations within a species’ range could enhance recruitment and facilitate upslope range shifts with climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate

Abstract This study investigates the effects of irrigation on global water resources by performing and analyzing Community Land Model 4.0 (CLM4) simulations driven by downscaled/bias‐corrected historical simulations and future projections from five General Circulation Models (GCMs). For each climate scenario, three sets of numerical experiments were performed: (1) a CTRL experiment in which all crops are assumed to be rainfed; (2) an IRRIG experiment in which the irrigation module is activated using surface water (SW) to feed irrigation; and (3) a PUMP experiment in which a groundwater pumping scheme coupled with the irrigation module is activated for conjunctive use of surface water and groundwater (GW) for irrigation. The parameters associated with irrigation and groundwater pumping are calibrated based on a global inventory of census‐based water use compiled by the Food and Agricultural Organization (FAO). Our results suggest that irrigation could lead to two major effects: SW (GW) depletion in regions with irrigation primarily fed by SW (GW), respectively. Furthermore, irrigation depending primarily on SW tends to have larger impacts on low‐flow than high‐flow conditions, suggesting increased vulnerability to drought. By the end of the 21st century, combined effect of increased irrigation water demand and amplified temporal‐spatial variability of water supply may lead to severe local water scarcity for irrigation. Regionally, irrigation has the potential to aggravate/alleviate climate‐induced changes of SW/GW although such effects are negligible when averaged globally. Our study highlights the need to account for irrigation effects and sources in assessing regional climate change impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

17 WIND ENERGY↗