Search NASA⌕ Search

SEARCH · Search NASA

Results for “Observing System Simulation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum computing universal thermalization dynamics in a (2 + 1)D Lattice Gauge Theory

Simulating non-equilibrium phenomena in strongly-interacting quantum many-body systems, including thermalization, is a promising application of near-term and future quantum computation. By performing experiments on a digital quantum computer consisting of fully-connected optically-controlled trapped ions, we study the role of entanglement in the thermalization dynamics of a Z 2 lattice gauge theory in 2+1 spacetime dimensions. Using randomized-measurement protocols, we efficiently learn a classical approximation of non-equilibrium states that yields the gap-ratio distribution and the spectral form factor of the entanglement Hamiltonian. These observables exhibit universal early-time signals for quantum chaos, a prerequisite for thermalization. Our work, therefore, establishes quantum computers as robust tools for studying universal features of thermalization in complex many-body systems, including in gauge theories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Comparative Study of Cloud Properties Between Northern and Southern High Latitudes Based on ARM Observations and EAMv2 Simulations

Earth's high-latitude regions show a considerable amount of cloud coverage, which significantly affects global radiation budgets. Here, in this study, we examine ground- and ship-based remote sensing observations from two Atmospheric Radiation Measurement (ARM) field campaigns conducted in the high-latitude regions of Northern and Southern Hemispheres (NH and SH). A comparison of macrophysical properties for low, mid-level, high, and deep clouds shows that the cloud top heights and thickness values are higher in the Arctic for each cloud type compared with the Southern Ocean and Antarctic regions, possibly due to higher sea surface temperatures in the north Atlantic. The Arctic also shows lower ice phase occurrence frequencies compared with the SH high latitudes for low clouds. For high, mid-level and deep clouds, the cloud phase distributions as a function of temperature show small geographical variabilities. The observations are then compared with the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) to evaluate the representations of these cloud properties. Here, the results show that the model captures cloud base and top heights well and reproduces the differences in cloud macrophysical properties between the two hemispheres. But the model underestimates ice phase frequencies for all cloud types. Such model cloud phase bias is strongly correlated with positive relative humidity biases and negative aerosol number concentration biases, indicating an important role of relative humidity and aerosols in modulating cloud phase partitioning in the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Role of Leaf Area Changes Within Plant CO 2 Physiological Impacts on the Global Hydrological Cycle

Rising atmospheric CO 2 concentrations enhance greenhouse warming and drive changes to plant physiology, leading to innumerable climate impacts. This study explores the impacts of plant responses on hydrological cycling at 2x preindustrial CO 2 concentrations by analyzing simulations that isolate plant physiological effects using the Community Earth System Model versions 1 and 2. We find that leaf area growth increases canopy evaporation, which offsets transpiration declines, and dampens changes in global mean evapotranspiration, precipitation, and runoff in a CESM2 experiment with dynamic leaf area. These leaf area impacts are also evident in the differences between CESM1 and CESM2, with CESM2 better capturing observed leaf area magnitudes but potentially overestimating leaf area-CO 2 sensitivity, highlighting the importance of plant CO 2 physiology on hydrological cycle changes and the need to improve its representation in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimizing the critical temperature and superfluid density of a metal-superconductor bilayer

A promising path to realizing higher superconducting transition temperatures 𝑇 c is the strategic engineering of artificial heterostructures. For example, quantum materials could, in principle, be coupled with other materials to produce a more robust superconducting state. Here, in this work, we add numerical support to the hypothesis that a strongly interacting superconductor weakened by phase fluctuations can boost its 𝑇 c by hybridizing the system with a metal. Using determinant quantum Monte Carlo, we simulate a two-dimensional bilayer composed of an attractive Hubbard model and a metallic layer in two regimes of the interaction strength −|𝑈|. In the strongly interacting regime, we find that increasing the interlayer hybridization 𝑡 ⊥ results in a nonmonotonic enhancement of 𝑇 c , with an optimal value comparable to the maximum 𝑇 c observed in the single-layer attractive Hubbard model, confirming trends inferred from other approaches. In the intermediate coupling regime, when −|𝑈| is close to the value associated with the maximum 𝑇 c of the single-layer model, increasing 𝑡 ⊥ tends to decrease 𝑇 c , implying that the correlated layer was already optimally tuned. Importantly, we demonstrate that the mechanism behind these trends is related to enhancement in the superfluid stiffness, as was initially proposed by Kivelson [Phys. B: Condens. Matter 318, 61 (2002)].

Zhang, Yutan [Univ. of California, Davis, CA (Unit↗
DOE OSTI2026

HTGR Multiphysics Application Drivers FY26 Updates

This report summarizes FY26 progress under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program's high-temperature gas-cooled reactor (HTGR) application driver work, covering a wide range of activities such as code validation and multi-physics code assessment. 1) A detailed SAM model of the High-Temperature Engineering Test Reactor (HTTR) was developed using a unique-block grouping approach, with an extended parallel thermal network method to capture block-to-block conduction and radiation heat transfer, and applied to steady-state simulations of the HTTR 30~MW and 9~MW cases. 2) In another activity, SAM's newly implemented multi-component gas flow model was validated against the Natural convection Shutdown heat removal Test Facility (NSTF) argon ingress experiment, correctly capturing the density-driven suppression and thermal recovery of natural circulation observed when argon is introduced into the air-cooled Reactor Cavity Cooling System (RCCS) loop. 3) For the OECD/NEA High Temperature Test Facility (HTTF) benchmark, we co-led the international benchmark activities as well as the OECD/NEA final benchmark report to be released at the end of this year. 4) Finally, the coupled Griffin-SAM modeling capability for pebble-bed HTGRs was advanced by verifying the Griffin neutronics solution against Serpent Monte Carlo for a realistic non-uniform temperature distribution, resolving several deficiencies in the SAM-to-Griffin temperature transfer scheme, and enabling distinct fuel kernel, moderator, and coolant temperatures for cross section feedback. These new features were demonstrated in a PBR load-following transient.

Lee, Alvin↗
DOE OSTI2025

Breakdown of superdiffusion in perturbed quantum integrable spin chains and ladders

Superdiffusive transport with dynamical exponent z=3/2 has been firmly established at finite temperature for a class of integrable systems with a non-Abelian global symmetry G. On the inclusion of integrability-breaking perturbations, diffusive transport with z=2 is generically expected to hold in the limit of late time. Recent studies of the classical Haldane-Ishimori-Skylanin model have found that perturbations that preserve the global symmetry lead to a much slower time scale for the onset of diffusion, albeit with uncertainty over the exact scaling exponent. That is, for perturbations of strength λ, the characteristic timescale for diffusion goes as t*∼λ−α for some α. Using large-scale matrix product state simulations, we investigate this behavior for perturbations to the canonical quantum model showing superdiffusion: the S=1/2 quantum Heisenberg chain. We consider a ladder configuration and look at various perturbations that either break or preserve the SU(2) symmetry, leading to scaling exponents consistent with those observed in one classical study [McCarthy , Phys. Rev. B 110, L180301 (2024)2469-995010.1103/PhysRevB.110.L180301]: α=2 for symmetry-breaking terms and α=6 for symmetry-preserving terms. We also consider perturbations from another integrable point of the ladder model with G=SU(4) and find consistent results. Finally, we consider a generalization to an SU(3) ladder and find that the α=6 scaling appears to be universal across superdiffusive systems when the perturbations preserve the non-Abelian symmetry G.

Wang, Kevin↗
DOE OSTI2026

Auriga Streams III: the mass–metallicity relation does not rule out tidal mass-loss in Local Group satellites

The mass–metallicity relation is a fundamental galaxy scaling law that has been extended to the faintest systems in the Local Group. We show that the small scatter in this relation, which has been used to argue against tidal mass-loss in Local Group satellites, is consistent with the level of disruption in the Auriga simulations. For every accreted system in Auriga, we compute stellar masses and metallicities two ways: considering the total system (bound + lost material) and only considering the progenitor. Accreted systems in Auriga have a tight relation between total stellar mass and metallicity, with scatter at a fixed stellar mass driven by age. When only considering the progenitor, the tidally evolved mass–metallicity relation has similar scatter (⁠~0.27 dex) as observed for the Local Group satellites (⁠~0.23 dex). Satellites that lie above the evolved relation have experienced substantial mass-loss and typically have low metallicity for their total stellar mass. Even satellites that fall exactly on the evolved relation can lose over half of their stellar mass. Only satellites substantially below the evolved relation are reliably intact. Based on their offset from the observed relation, we predict which Milky Way and M31 satellites have tidal tails waiting to be discovered.

Galaxy: halo↗
DOE OSTI2024

Freezing Processes in Southern Ocean Mixed Phase Clouds (Final Technical Report)

Southern Ocean (SO) low-level clouds remain a challenge for Earth system models to represent accurately. This project utilized DOE ARM observations and the Community Atmosphere Model (CAM) to improve process-level understanding of aerosol-cloud interactions and cloud microphysics for SO mixed-phase clouds. Specifically, our efforts include two main topics: 1) investigate the SO aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) population based on observations and determine predictive skill of simulating SO INPs, and 2) assess the ice formation pathways, including ice nucleation and secondary ice production, in SO mixed phase clouds utilizing a combination of observations and modeling tools. Studies focused on SO aerosol and INP resulted four manuscripts. Ice formation pathways in SO mixed phase clouds were investigated, revealing observed evidence of SIP active across all mixed phase temperatures. However, a key finding of this project is that despite significant progress in predicting SO INPs, the role of INP variability in SO cloud phase, precipitation, and radiative properties remain unknown due to challenges in microphysics parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models

Land surface relative humidity (RH) is a key variable in the coupled land–atmosphere system that profoundly influences terrestrial hydroclimate and ecosystems. Yet historical changes in land RH are not well understood due to limited observations, biased reanalyses, and the lack of a framework for interpreting RH changes under multiple influencing factors. Here, we show that the spatiotemporal variability of land RH and its distinct historical trends among observations, reanalyses, and Earth system models are captured by a simple index based on the ratio of precipitation (P) to a modified potential evapotranspiration formulated independently of RH (PET°). The index provides a physical calibration of biased land RH in reanalyses and a quantitative framework for interpreting land RH changes. Over 1973–2024, land RH has decreased substantially, owing to the intrinsic rise in PET° with temperature and little increase in land precipitation. Reanalyses overestimate the observed RH decrease, consistent with exaggerated surface warming and precipitation decline. The index captures this coherent bias and enables a calibration using observed precipitation and temperature. Models simulate a wide range of land RH trends, but nearly all runs underrepresent the historical drying. The index captures the model spread and discrepancy and attributes them to contributions of precipitation and PET°. Weaker land RH decreases in models arise mainly from weaker subtropical precipitation declines, linked to muted intensification of subtropical highs and biased subtropical climatology. The model–observation discrepancy is unlikely explained by internal variability, implying model underestimation of forced RH decrease and a drier land future than current projections.

Earth system models↗
DOE OSTI2024

Mechanisms of Three-Dimensional Solid-Phase Epitaxial Crystallization of Strontium Titanate

Strontium titanate (SrTiO 3 , STO) is a complex metal oxide with a cubic perovskite crystal structure. Due to its easily described and understood crystal structure in the cubic phase, STO is an ideal model system for exploring the mechanistic details of solid-phase epitaxy (SPE) in complex oxides. SPE is a crystallization approach that aims to guide crystal growth at low homologous temperatures to achieve targeted microstructures. Beyond planar thin films, SPE can also exploit the addition of a chemically inert, noncrystallizing, amorphous obstacle in the path of crystallization to generate complex three-dimensional structures. The introduction of this mask fundamentally alters the SPE process, inducing a transition from two- to three-dimensional geometries and from vertical to lateral crystal growth under the influence of the crystal/mask/amorphous boundary. Using a combination of molecular dynamics simulations and experiments, we identify several unique phenomena in the nanoscale growth behaviors in both conventional (unmasked) and masked SPE. Examining conventional SPE of STO, we find that crystallization at the interface is strongly correlated to, and potentially driven by, density fluctuations in the region of the amorphous STO near the crystalline/amorphous interface with a strong facet dependence. In the masked case, we find that the crystalline growth front becomes nonplanar near contact with the mask. We also observe a minimum vertical growth requirement prior to lateral crystallization. Both phenomena depend on the relative bulk and interfacial free energies of the three-phase (crystal/mask/amorphous) system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

2D kinetic-ion simulations of inverted corona fusion targets

Laser-driven “inverted corona” fusion targets have attracted interest as a low-convergence neutron source and platform for studying kinetic physics. The scheme consists of a hollow or gas-filled spherical shell made of deuterated plastic. The shell has one or more laser entrance holes (LEH), resembling a spherical hohlraum. The laser passes through the LEH’s and illuminates the interior surface of the shell, ablating a plasma that travels inward towards the target center. Long ion mean free paths in the converging plasma can lead to significant interpenetration, atomic mix, and other kinetic effects. Here, in this work we report on numerical simulations of inverted corona targets using the kinetic-ion, fluid–electron hybrid particle-in-cell (PIC) approach in 2D RZ geometry. 2D simulations suggest that shape effects do not have a significant impact on plasma evolution and observed yield trends are primarily the result of 1D kinetic mix mechanisms. Simulations are also compared against available experimental data recorded at the OMEGA laser facility. In particular, synthetic x-ray emission images show good qualitative agreement with experimental results, albeit with an apparent timing discrepancy for the two-sided vacuum target. More generally, we demonstrate the potential of hybrid-PIC simulations for full-system modeling and experimental design, including collisional absorption of laser energy, plasma evolution, mix, and fusion burn.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?

Abstract The historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Dynamic Carrier Modulation via Nonlinear Acoustoelectric Transport in van der Waals Heterostructures

Dynamically manipulating carriers in van der Waals heterostructures could enable solid-state quantum simulators with tunable lattice parameters. A key requirement is the formation of deep potential wells to reliably trap excitations. Here, we report the observation of nonlinear acoustoelectric transport and dynamic carrier modulation in boron nitride-encapsulated graphene devices coupled to intense surface acoustic waves (SAWs) on LiNbO 3 substrates. SAWs generate strong acoustoelectric current densities ( J AE ), transitioning from linear to nonlinear regimes with increasing SAW intensity. In the nonlinear regime, periodic carrier (electrons, holes, or their mixtures) stripes emerge. Using counter-propagating SAWs, we create standing SAWs (SSAWs) to dynamically manipulate charge distributions without static gates. The saturation of J AE , attenuation transitions, and tunable resistance peaks confirms strong carrier localization. Finally, these results establish SAWs as a powerful tool for controlling carrier dynamics in two-dimensional (2D) materials, paving the way for the development of time-dependent quantum systems and acoustic lattices for quantum simulation.

2D materials↗
DOE OSTI2025

Improving the QBO Forcing by Resolved Waves With Vertical Grid Refinement in E3SMv2

Abstract The quasi‐biennial oscillation (QBO) is the dominate mode of variability in the tropical stratosphere and plays an important role in stratospheric dynamics and chemistry. The QBO is notably deficient in many climate models, including the Energy Exascale Earth System Model (E3SM) developed by the US Department of Energy. In this work, we refine the lower stratospheric vertical grid spacing from roughly 1 km to 500 m to facilitate more realistic equatorial wave activity in the lower stratosphere in E3SM version 2. The refinement results in a simulated QBO with a reasonable amplitude and easterly‐westerly transition in both directions, but still has a longer period than observed, slower easterly downward propagation speed, and shallower vertical depth. Similar refinement in the multi‐scale modeling framework configuration of E3SM yields similar improvements. By analyzing the forcing contributions from different wave types, we find that most of the QBO forcing still comes from parameterized gravity wave drag from convection. The improved QBO forcing contributions from resolved waves, especially equatorial Kelvin waves and resolved small scale waves, can be attributed to the grid refinement.

E3SM↗
DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗
DOE OSTI2024

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA-Based Surrogate for Spatial Fields

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

54 ENVIRONMENTAL SCIENCES↗