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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Near-term tropical cyclone risk and coupled Earth system model biases

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas–induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models’ responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios—including some in which recent historical trends continue—even if such projections cannot currently be produced using existing coupled earth system models.

Science & Technology - Other Topics↗
DOE OSTI2021

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

To Exascale and Beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud-Resolving Scales

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large-Eddy Simulations of Low-Level Warm Clouds

In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate (Au), which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local Au is commonly parameterized as a function of DSD moments: cloud droplet number (n c ) and mass (q c ) concentrations. When applied in a large-scale model, the grid-mean Au must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large-eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local Au, as well as its spatial variability. The analysis of an ensemble of warm low-level cloud cases shows that the E3SMv2 formulation represents the Au reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM-tuned local Au rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of n c and its co-variability with q c in parameterizing the grid-mean Au.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using ARM Observations to Evaluate Process-Interactions in MCS Simulations Across Scales (Final Progress Report)

This project, funded by DOE Atmospheric System Research (DE-SC0020050), focused on improving the representation of mesoscale convective systems (MCSs) in numerical weather and climate models by leveraging high-resolution observations from the DOE Atmospheric Radiation Measurement (ARM) program. The research aimed to evaluate model sensitivities to grid spacing, microphysics, and planetary boundary layer (PBL) schemes, with a particular emphasis on improving convection parameterization for high-resolution modeling. Findings from this work highlight several key advancements. Model validation against ARM radar wind profiler data from the Southern Great Plains (SGP) and Manaus (MAO) sites revealed systematic biases in simulated convective mass flux profiles, leading to the development of an observationally constrained evaluation framework for diagnosing and improving model performance. Sensitivity analyses demonstrated that the representation of Amazonian MCSs was highly dependent on PBL scheme selection, while mid-latitude MCSs were more strongly influenced by microphysics parameterizations. A series of high-resolution WRF simulations, ranging from 4 km to 125 m grid spacing, provided insight into the behavior of convective drafts across scales. While updraft properties converged at sub-kilometer resolutions, biases in downdraft intensity persisted even at the finest resolution tested, emphasizing the need for further refinements in model physics. Additionally, comparisons of MCS vertical structures between mid-latitude and tropical environments revealed stronger updrafts and larger mass flux in mid-latitude MCSs, providing critical insights for improving climate model representations of storm-scale dynamics. The project’s findings have already contributed to advancing numerical modeling capabilities, particularly in WRF, MPAS, ICON, and DOE’s SCREAM model, by refining how convective processes are represented in high-resolution climate simulations. Results were disseminated through peer-reviewed publications, conference presentations, and ARM/ASR Research Highlights, engaging the broader scientific community. The project also provided valuable training opportunities for two postdoctoral researchers, who played central roles in model development, analysis, and dissemination of results. Their work contributed to several publications and conference presentations, helping prepare them for careers in atmospheric modeling. By improving the simulation of MCSs, this research directly supports the development of next-generation climate models capable of more accurately representing extreme precipitation and convective processes. The insights gained will inform future improvements in convective parameterization and guide the design of high-resolution weather and climate simulations, ultimately enhancing the reliability of climate projections and weather forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Integrated hydrological, power system and economic modelling of climate impacts on electricity demand and cost

Impacts of climate-related water stress and temperature changes can cascade through energy systems, although models have yet to capture this compounding of effects. Here, we employ a coupled water–power–economy model to capture these important interactions in a study of the exceedance of water temperature thresholds for power generation in the western United States. We find that not all reductions in reserve electricity-generation capacity result in impacts, and that when they occur, intermittent interruptions in electricity supply at critical times of the day, week and year account for much of the economic impacts. Finally, we find that impacts may be in different locations from the original water stress. Herein, we estimate that the consumption loss can be up to 0.3% annually and the drivers identified in coupled modelling can increase the average cost of electricity by up to 3%. Integrated models will be needed to capture the cascading effects of climate change through climatic, water, energy and economic systems. Webster et al. now develop a coupled hydrologic–power-production–economic model to estimate water-stress impacts on electricity cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio-economic sectors

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Cli mate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Potential Effects of Climate Change and Solar Radiation Modification on Renewable Energy Resources

Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect incoming solar radiation with the goal of reducing global temperatures, which have increased over the last decades due to high atmospheric greenhouse gas concentrations. Stratospheric aerosol injection, specifically, has shown potential for successfully reducing global temperatures in climate model simulations. Despite the growing literature in the areas of climate change and SRM, their combined effects on renewable energy generation, a climate change mitigation strategy, have not been addressed. In this review paper, we synthesize previous literature on the possible effects of climate change and SRM on renewable energy resources (i.e., wind energy, solar energy, biomass energy, and hydropower), review the status of climate change and SRM research, and explore potential effects of SRM on renewable energy primarily in the Continental United States (CONUS), but with global perspectives as well. We discuss the research challenges and impacts of SRM on renewable energy and conclude by discussing the potential implications of SRM for renewables for SRM governance and policy. This work is not advocating for or against SRM. It is highlighting an important potential impact for future decision makers.

Kumler, Andrew [National Renewable Energy Laborato↗
DOE OSTI2021

ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution using the Community Ice Sheet Model

Abstract. The future retreat rate for marine-based regions of the Antarctic Ice Sheet is one of the largest uncertainties in sea-level projections. The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) aims to improve projections and quantify uncertainties by running an ensemble of ice sheet models with atmosphere and ocean forcing derived from global climate models. Here, the Community Ice Sheet Model (CISM) is used to run ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution. Using multiple combinations of sub-ice-shelf melt parameterizations and calibrations, CISM is spun up to steady state over many millennia. During the spin-up, basal friction parameters and basin-scale thermal forcing corrections are adjusted to optimize agreement with the observed ice thickness. The model is then run forward for 550 years, from 1950–2500, applying ocean thermal forcing anomalies from six climate models. In all simulations, the ocean forcing triggers long-term retreat of the West Antarctic Ice Sheet, especially in the Filchner–Ronne and Ross sectors. Mass loss accelerates late in the 21st century and then rises steadily for several centuries without leveling off. The resulting ocean-forced sea-level rise at year 2500 varies from about 150 to 1300 mm, depending on the melt scheme and ocean forcing. Further experiments show relatively high sensitivity to the basal friction law, moderate sensitivity to grid resolution and the prescribed collapse of small ice shelves, and low sensitivity to the stress-balance approximation. The Amundsen sector exhibits threshold behavior, with modest retreat under many parameter settings but complete collapse under some combinations of low basal friction and high thermal forcing anomalies. Large uncertainties remain, as a result of parameterized sub-shelf melt rates, simplified treatments of calving and basal friction, and the lack of ice–ocean coupling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimates of Southern Hemispheric Gravity Wave Momentum Fluxes across Observations, Reanalyses, and Kilometer-Scale Numerical Weather Prediction Model

Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Climate-extreme modeling framework for sustainable flood management in the Arabian Peninsula

Evaluating extreme precipitation events (EPEs) is essential for building climate-resilient water management strategies, but it remains a major challenge in ungauged basins. Using the 26,070 km 2 Wadi al-Rummah basin in central Saudi Arabia as a case study, we developed an alternative, reliable, cost-effective satellite-based framework that combines empirically derived EPE thresholds, imagery-calibrated 2D hydrodynamic modeling, GRACE water-storage diagnostics, and bias-corrected CMIP6 projections to assess flood hazards and recharge potential under current and future climate scenarios in ungauged basins. The integrated approach and the resulting findings followed four key steps: (1) Identified a 22.5 mm EPE threshold, the 80th percentile of 3-day GPM/IMERG rainfall (2000–2024), aligned with flood-triggering events (Nov 2018: 23–28 mm; Apr, 2023: 42 mm); (2) Developed and calibrated a RiverFlow2D model using Sentinel-2 and PlanetScope imagery for the November 2018 flood, accurately reproducing flood depth and extent (RMSE ≤0.31 m; fuzzy-Dice ≥0.91), and estimating runoff (41 %), infiltration (25 %), and evaporation (34 %); (3) Independently validated the model with the April 2023 event (RMSE ≤0.35 m; fuzzy-Dice ≥0.86); (4) Conducted climate projections (2025–2100) from five bias-corrected NEX-GDDP CMIP6 models that revealed a 34 % increase in EPE intensity under SSP2-4.5 and 48 % under SSP5-8.5 scenarios, relative to 20th-century baselines. Our findings indicate that while intensifying extremes in the 21st century increase flood risk, the results highlight the potential for episodic recharge if effective retention strategies are employed, and offer a transferable model for climate-informed planning in data-scarce arid regions.

CMIP6↗
DOE OSTI2022

When Will MISR Detect Rising High Clouds?

It is predicted by both theory and models that high-altitude clouds will occur higher in the atmosphere as a result of climate warming. This produces a positive longwave feedback and has a substantial impact on the Earth's response to warming. This effect is well established by theory, but is poorly constrained by observations, and there is large spread in the feedback strength between climate models. For this study, we use the NASA Multi-angle Imaging SpectroRadiometer (MISR) to examine changes in Cloud-Top-Height (CTH). MISR uses a stereo-imaging technique to determine CTH. This approach is geometric in nature and insensitive to instrument calibration and therefore is well suited for trend analysis and studies of variability on long time scales. In this article we show that the current MISR record does have an increase in CTH for high-altitude cloud over Southern Hemisphere (SH) oceans but not over Tropical or the Northern Hemisphere (NH) oceans. We use climate model simulations to estimate when MISR might be expected to detect trends in CTH, that include the NH. The analysis suggests that according to the models used in this study MISR should detect changes over the SH ocean earlier than the NH, and if the model predictions are correct should be capable of detecting a trend over the Tropics and NH very soon (3–10 years). This result highlights the potential value of a follow-on mission to MISR, which no longer maintains a fixed equator crossing time and is unlikely to be making observations for another 10 years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Effective radiative forcing and adjustments in CMIP6 models

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Continental United States climate projections based on thermodynamic modification of historical weather

Abstract Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using multiple high-resolution datasets to benchmark the energy exascale earth system model (E3SM) for renewable resource assessment

The United States is accelerating its shift toward a renewable energy system. However, renewable resources, which harness energy from the Earth system, are susceptible to both present-day climate variability and future climate change. For example, variations in regional climate can alter renewable energy production patterns and site viability. The use of high-resolution climate model projections can therefore facilitate and may be critical to long-term planning of renewable energy investments. However, climate models must first be validated for renewable resource assessment. This research employs multiple high-spatiotemporal-resolution datasets to assess the capability of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 North American Regionally Refined Model (E3SMv2-NARRM) for predicting multi-year climatological values of solar and wind energy capacity factors in the continental U.S., with a focus on regional and seasonal variability. Present-day E3SMv2-NARRM simulations are compared with reported utility-scale production data obtained from the Energy Information Administration (EIA). In addition, E3SMv2-NARRM data are evaluated against non-climate benchmark models from the National Renewable Energy Laboratory, including the Wind Integration National Dataset Toolkit and the National Solar Radiation Database (NSRDB), as well as three wind energy datasets from PLUSWIND. Our analysis indicates that solar capacity factors from E3SM closely match those from the NSRDB dataset. However, both datasets tend to overestimate values by 10% in comparison to EIA data. Furthermore, biases in wind capacity factors within E3SM are notably pronounced in the West Coast regions, where the seasonal cycle diverges from EIA data.

Energy forecasting, Capacity factor, Renewable ene↗
DOE OSTI2024

Quantifying and Modeling the Impact of Phase State on the Ice Nucleation Abilities of 2-Methyltetrols as a Key Component of Secondary Organic Aerosol Derived from Isoprene Epoxydiols

Organic aerosols (OAs) may serve as ice-nucleating particles (INPs), impacting the formation and properties of cirrus clouds when their phase state and viscosity are in the semisolid to glassy range. However, there is a lack of direct parameterization between aerosol viscosity and their ice nucleation capabilities. In this study, we experimentally measured the ice nucleation rate of 2-methyltetrols (2-MT) aerosols, a key component of isoprene-epoxydiol-derived secondary organic aerosols (IEPOX-SOA), at different viscosities. These results demonstrate that the phase state has a significant impact on the ice nucleation abilities of OA under typical cirrus cloud conditions, with the ice nucleation rate increasing by 2 to 3 orders of magnitude when the phase state changes from liquid to semisolid. An innovative parametric model based on classical nucleation theory was developed to directly quantify the impact of viscosity on the heterogeneous nucleation rate. This model accurately represents our laboratory measurement and can be implemented into climate models due to its simple, equation-based form. Based on data collected from the ACRIDICON-CHUVA field campaign, our model predicts that the INP concentration from IEPOX-SOA can reach the magnitude of 1 to tens per liter in the cirrus cloud region impacted by the Amazon rainforest, consistent with recent field observations and estimations. This novel parameterization framework can also be applied in regional and global climate models to further improve representations of cirrus cloud formation and associated climate impacts.

2-methyltetrol↗
DOE OSTI2022

Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site

Abstract. Complex distributions of aerosol properties evolve in space and time as a function of emissions, new particle formation, coagulation, condensational growth, chemical transformation, phase changes, turbulent mixing and transport, removal processes, and ambient meteorological conditions. The ability of chemical transport models to represent the multi-scale processes affecting the life cycle of aerosols depends on their spatial resolution since aerosol properties are assumed to be constant within a grid cell. Subgrid-scale-dependent processes that affect aerosol populations could have a significant impact on the formation of particles, their growth to cloud condensation nuclei (CCN) sizes, aerosol–cloud interactions, dry deposition and rainout and hence their burdens, lifetimes, and radiative forcing. To address this issue, we characterize subgrid-scale variability in terms of measured aerosol number, size, composition, hygroscopicity, and CCN concentrations made by repeated aircraft flight paths over the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site during the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystem (HI-SCALE) campaign. Subgrid variability is quantified in terms of both normalized frequency distributions and percentage difference percentiles using grid spacings of 3, 9, 27, and 81 km that represent those typically used by cloud-system-resolving models as well as the current and next-generation climate models. Even though the SGP site is a rural location, surprisingly large horizontal gradients in aerosol properties were frequently observed. For example, 90 % of the 3, 9, and 27 km cell mean organic matter concentrations differed from the 81 km cell around the SGP site by as much as ∼ 46 %, large spatial variability in aerosol number concentrations and size distributions were found during new particle formation events, and consequently 90 % of the 3, 9, and 27 km cell mean CCN number concentrations differed from the 81 km cell mean by as much as ∼ 38 %. The spatial variability varied seasonally for some aerosol properties, with some having larger spatial variability during the spring and others having larger variability during the late summer. While measurements at a single surface site cannot reflect the surrounding variability of aerosol properties at a given time, aircraft measurements that are averaged within an 81 km cell were found to be similar to many, but not all, aerosol properties measured at the ground SGP site. This analysis suggests that it is reasonable to directly compare most ground SGP site aerosol measurements with coarse global climate model predictions. In addition, the variability quantified by the aircraft can be used as an uncertainty range when comparing the surface point measurements with model predictions that use coarse grid spacings.

54 ENVIRONMENTAL SCIENCES↗