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End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗

DOE OSTI2018

Uncertainty Analysis in 3D Equilibrium Reconstruction

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the results of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

The 1995 scientific assessment of the atmospheric effects of stratospheric aircraft

This report provides a scientific assessment of our knowledge concerning the impact of proposed high-speed civil transport (HSCT) aircraft on the atmosphere. It comes at the end of Phase 1 of the Atmospheric Effects of Stratospheric Aircraft element of the NASA High-Speed Research Program. The fundamental problem with stratospheric flight is that pollutant residence times are long because the stratosphere is a region of permanent temperature inversion with stable stratification. Using improved two-dimensional assessment models and detailed fleet emissions scenarios, the assessment examines the possible impact of the range of effluents from aircraft. Emphasis is placed on the effects of NO(x) and H2O on the atmospheric ozone content. Measurements in the plume of an in-flight Concorde supersonic transport indicated a large number of small particles. These measurements, coupled with model sensitivity studies, point out the importance of obtaining a more detailed understanding of the fate of sulfur in the HSCT exhaust. Uncertainties in the current understanding of the processes important for determining the overall effects of HSCT's on the atmosphere are discussed and partially quantified. Research directions are identified to improve the quantification of uncertainties and to reduce their magnitude.

Stolarski, Richard S.↗

DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Surface Crystallization of Supercooled Water in Clouds

The process by which liquid cloud droplets homogeneously crystallize into ice is still not well-understood. The ice nucleation process based on the standard and classical theory of homogeneous freezing, initiates within the interior volume of a cloud droplet. Current experimental data on homogeneous freezing rates of ice in droplets of supercooled water, both in air and emulsion oil samples, show considerable scatter. For example, at -33 C, the reported volume-based freezing rates of ice in supercooled water vary by as much as 5 orders of magnitude, which is well outside the range of measurement uncertainties. Here, we show that the process of ice nucleus formation at the air (or oil)-liquid water interface may help to explain why experimental results on ice nucleation rates yield different results in different ambient phases. Our results also suggest that surface crystallization of ice in cloud droplets can explain why low amounts of supercooled water have been observed in the atmosphere near -40 C.

Tabazadeh, Azadeh↗

DOE OSTI2018

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Evaluation of Neutron-induced Cross Sections and their Related Covariances with Physical Constraints

Nuclear data, along with numerical methods and the associated calculation schemes, continue to play a key role in reactor design, reactor core operating parameters calculations, fuel cycle management and criticality safety calculations. Due to the intensive use of Monte-Carlo calculations reducing numerical biases, the final accuracy of neutronic calculations increasingly depends on the quality of nuclear data used. This paper gives a broad picture of all ingredients treated by nuclear data evaluators during their analyses. After giving an introduction to nuclear data evaluation, we present implications of using the Bayesian inference to obtain evaluated cross sections and related uncertainties. In particular, a focus is made on systematic uncertainties appearing in the analysis of differential measurements as well as advantages and drawbacks one may encounter by analyzing integral experiments. The evaluation work is in general done independently in the resonance and in the continuum energy ranges giving rise to inconsistencies in evaluated files. For future evaluations on the whole energy range, we call attention to two innovative methods used to analyze several nuclear reaction models and impose constraints. Finally, we discuss suggestions for possible improvements in the evaluation process to master the quantification of uncertainties. These are associated with experiments (microscopic and integral), nuclear reaction theories and the Bayesian inference.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Performance assessment modeling of a generic SNF/HLW repository in salt with coupled thermal-hydrologic effects - 15423

This paper describes advances in performance assessment modeling of deep geologic repositories facilitated by a massively parallel, high-performance computing (HPC) environment. Our new Generic Disposal System Analysis (GDSA) Framework utilizes the massively parallel PFLOTRAN multi-physics code to simulate repository performance in the presence of coupled thermal-hydrologic-chemical processes, linked to DAKOTA, an HPC uncertainty sampling and propagation code that provides nested parallelism for multi-realization performance assessment and sensitivity analysis. This enhanced performance assessment (PA) modeling capability is demonstrated with deterministic and probabilistic simulations of a generic repository for SNF and HLW in bedded salt host rock, by comparing repository performance between a case with heat-generating waste ('thermal' case) and a case without heat generation ('isothermal' case). The simulation results provide preliminary insights into the effect of multi-physics processes and thermal-hydrologic coupling on the long-term behavior of a reference-case salt repository. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Subglacial drainage models represent water flow at the ice–bed interface through coupled distributed and channelized systems to determine water pressure, discharge, and drainage system geometry. While they are used to understand processes such as the relationship between surface melt and ice flow, the number of uncertain model parameters and the computational cost of running models makes it difficult to adequately explore the high-dimensional parameter space and evaluate uncertainty in model predictions. Here, we develop Gaussian process (GP) emulators that make fast predictions with associated uncertainty of subglacial drainage model outputs. Using a truncated principal component (PC) basis representation, we construct a GP emulator for diurnally averaged subglacial water pressure. We also explore emulation of scalar variables describing drainage efficiency and configuration. We train the emulators using ensembles of up to 512 simulations varying eight parameters of the Glacier Drainage System (GlaDS) model on a synthetic domain intended to represent an ice-sheet margin. The emulators make predictions ∼ 1000 times faster than GlaDS simulations, with errors <3 % for the water pressure field and ∼ 5 %–9 % for drainage efficiency and configuration. We apply the emulators to explore the eight-dimensional parameter space by computing variance-based parameter sensitivity indices, finding that three parameters (ice flow coefficient, bed bump aspect ratio, and the subglacial cavity system conductivity) explain 90 % of the variance in modelled water pressure in response to parameter changes. The GP emulator approach described here is well suited to integrating observational data with models to make calibrated, credible predictions of subglacial drainage.

58 GEOSCIENCES↗

DOE OSTI2021

Propagation of Input Uncertainties in Numerical Simulations of Laser Powder Bed Fusion

Laser powder bed fusion has the potential of redefining state-of-the-art processing and production methods, but defect formation and inconsistent build quality have limited the implementation of this process. Numerical models are widely used to study this process and predict the formation of these defects. Presently, the uncertainties of model input parameters and thermophysical properties used by these numerical simulations have not been investigated. In the present study, the uncertainty in these input parameters and material properties are quantified for laser powder bed fusion, with and without a simulated powder bed, to determine their influence on the predictive accuracy of an experimentally validated numerical model. Accounting for all possible sources of uncertainty quickly becomes computationally expensive on account of the curse of dimensionality. Uncertainty in laser absorption, solid, and liquid specific heat of the metal were found to have the largest effect on model prediction reliability with or without the use of a powder bed. Results also illustrate that accounting for these three uncertain parameters still captures the majority of model prediction uncertainty. Furthermore, the methodology of this study may be used to understand the uncertainty in as-built microstructure through propagation to microstructure prediction models, or applied under processing conditions where high Péclet numbers are observed and the thermal convection and fluid flow within the molten pool are substantial.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Herschel dust emission as a probe of starless cores mass: MCLD 123.5+24.9 of the polaris flare

We present newly processed archival Herschel images of molecular cloud MCLD 123.5+24.9 in the Polaris Flare. This cloud contains five starless cores. Using the spectral synthesis code Cloudy, we explore uncertainties in the derivation of column densities, and hence masses of molecular cores from Herschel data. We first consider several detailed grain models that predict far-infrared grain opacities. Opacities predicted by the models differ by more than a factor of two, leading to uncertainties in derived column densities by the same factor. Then we consider uncertainties associated with the modified blackbody fitting process used by observers to estimate column densities. For high column density clouds (N(H) ≫ 1 × 10{sup 22} cm{sup −2}), this fitting technique can underestimate column densities by about a factor of three. Finally, we consider the virial stability of the five starless cores in MCLD 123.5+24.9. All of these cores appear to have strongly sub-virial masses, assuming, as we argue, that {sup 13}CO line data provide reliable estimates of velocity dispersions. Evidently, they are not self-gravitating, so it is no surprise that they are starless.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Application of stochastic optimization to nuclear power plant asset management decisions

We describe the development and application of stochastic optimization models and algorithms to address an issue of critical importance in the strategic allocation of resources; namely, the selection of a portfolio of capital investment projects under the constraints of a limited and uncertain budget. This issue is significant and one that faces decision-makers across all industries. The objective of this strategic decision process is generally self evident - to maximize the value obtained from the portfolio of selected projects (with value usually measured in terms of the portfolio's net present value). However, heretofore, many organizations have developed processes to make these investment decisions using simplistic rule-based rank-ordering schemes. This approach has the significant limitation of not accounting for the (often large) uncertainties in the costs or economic benefits associated with the candidate projects or in the uncertainties in the actual funds available to be expended over the projected period of time. As a result, the simple heuristic approaches that typically are employed in industrial practice generate outcomes that are non-optimal and do not achieve the level of benefits intended. In this paper we describe the results of research performed to utilize stochastic optimization models and algorithms to address this limitation by explicitly incorporating the evaluation of uncertainties in the analysis and decision making process. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Multiscale Modeling of Carbon Fiber Reinforced Polymer (CFRP) for Integrated Computational Materials Engineering Process

In this work, a multiscale modeling framework for CFRP is introduced to study hierarchical structure of CFRP. Four distinct scales are defined: nanoscale, microscale, mesoscale, and macroscale. Information at lower scales can be passed to higher scale, which is beneficial for studying effect of constituents on macroscale part’s mechanical property. This bottom-up modeling approach enables better understanding of CFRP from finest details. Current study focuses on microscale and mesoscale. Representative volume element is used at microscale and mesoscale to model material’s properties. At microscale, unidirection CFRP (UD) RVE is used to study properties of UD. The UD RVE can be modeled with different volumetric fraction to encounter non-uniform fiber distribution in CFRP part. Such consideration is important in modeling uncertainties at microscale level. Currently, we identified volumetric fraction as the only uncertainty parameters in UD RVE. To measure effective material properties of UD RVE, periodic boundary conditions (PBC) are applied to UD RVE to ensure convergence of obtained properties. Properties of UD is directly used at mesoscale woven RVE modeling, where each yarn is assumed to have same properties as UD. Within woven RVE, there can be many potential uncertainties parameters to consider for a physical modeling of CFRP. Currently, we will consider fiber misalignment within yarn and angle between wrap and weft yarns. PBC is applied to woven RVE to calculate its effective material properties. The effect of uncertainties are investigated quantitatively by Gaussian process. Preliminary results of UD and Woven study are analyzed for efficacy of the RVE modeling. This work is considered as the foundation for future multiscale modeling framework development for ICME project.

Composite, ICME, Multiscale Modeling↗

DOE OSTI2020

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗

NASA NTRS2009

Measurement and Characterization of Space Shuttle Solid Rocket Motor Plume Acoustics

Lift-off acoustic environments generated by the future Ares I launch vehicle are assessed by the NASA Marshall Space Flight Center (MSFC) acoustics team using several prediction tools. This acoustic environment is directly caused by the Ares I First Stage booster, powered by the five-segment Reusable Solid Rocket Motor (RSRMV). The RSRMV is a larger-thrust derivative design from the currently used Space Shuttle solid rocket motor, the Reusable Solid Rocket Motor (RSRM). Lift-off acoustics is an integral part of the composite launch vibration environment affecting the Ares launch vehicle and must be assessed to help generate hardware qualification levels and ensure structural integrity of the vehicle during launch and lift-off. Available prediction tools that use free field noise source spectrums as a starting point for generation of lift-off acoustic environments are described in the monograph NASA SP-8072: "Acoustic Loads Generated by the Propulsion System." This monograph uses a reference database for free field noise source spectrums which consist of subscale rocket motor firings, oriented in horizontal static configurations. The phrase "subscale" is appropriate, since the thrust levels of rockets in the reference database are orders of magnitude lower than the current design thrust for the Ares launch family. Thus, extrapolation is needed to extend the various reference curves to match Ares-scale acoustic levels. This extrapolation process yields a subsequent amount of uncertainty added upon the acoustic environment predictions. As the Ares launch vehicle design schedule progresses, it is important to take every opportunity to lower prediction uncertainty and subsequently increase prediction accuracy. Never before in NASA s history has plume acoustics been measured for large scale solid rocket motors. Approximately twice a year, the RSRM prime vendor, ATK Launch Systems, static fires an assembled RSRM motor in a horizontal configuration at their test facility in Utah. The remaining RSRM static firings will take place on elevated terrain, with the nozzle exit plume being mostly undeflected and the landscape allowing placement of microphones within direct line of sight to the exhaust plume. These measurements will help assess the current extrapolation process by direct comparison between subscale and full scale solid rocket motor data.

Kenny, Jeremy↗

DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗

DOE OSTI2025

Aerosol-Cloud Interactions and the Intersection of Climate Forcing and Feedback

The goal of this project has been to understand at the process level how the interaction of aerosols and cloud microphysics may alter the cloud response to climate change from the scale of turbulence to the global scale. Atmospheric aerosols (suspended particulates) have a unique role to play in the climate system. Anthropogenic aerosols may be an air pollution hazard and degrade human health. Their presence also results in the direct scattering or absorption of sunlight, with small net impacts on climate. But aerosols are also a key part of cloud formation, as the locations on which cloud droplets and ice crystals form. Understanding these Aerosol Cloud Interactions (ACI) is a critical uncertainty for climate models, including the Energy Exascale Earth System Model (E3SM), and affects how well models like E3SM can simulate the past and future. This work aims to understand cloud microphysics as well as the interaction of aerosol forcing and cloud feedback through improving our understanding of aerosol-cloud interactions (ACI) by looking at their uncertainty in critical environments at the process level. It will also contribute to the development of E3SM. Specific goals include: first, high level analysis of Perturbed Parameter Ensemble (PPE) simulations already available from the Community Earth System Model (CESM) and E3SM. Second, detailed analysis of recent changes to temperature and the role of aerosols.

54 ENVIRONMENTAL SCIENCES↗