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DOE OSTI2017

Gas Gun Model and Comparison to Experimental Performance of Pipe Guns Operating with Light Propellant Gases and Large Cryogenic Pellets

Injection of multiple large (~10 to 30 mm diameter) shattered pellets into ITER plasmas is presently part of the scheme planned to mitigate the deleterious effects of disruptions on the vessel components. To help in the design and optimize performance of the pellet injectors for this application, a model referred to as “the gas gun simulator” has been developed and benchmarked against experimental data. The computer code simulator is a Java program that models the gas-dynamics characteristics of a single-stage gas gun. Following a stepwise approach, the code utilizes a variety of input parameters to incrementally simulate and analyze the dynamics of the gun as the projectile is launched down the barrel. Using input data, the model can calculate gun performance based on physical characteristics, such as propellant-gas and fast-valve properties, barrel geometry, and pellet mass. Although the model is fundamentally generic, the present version is configured to accommodate cryogenic pellets composed of H2, D2, Ne, Ar, and mixtures of them and light propellant gases (H2, D2, and He). The pellets are solidified in situ in pipe guns that consist of stainless steel tubes and fast-acting valves that provide the propellant gas for pellet acceleration (to speeds ~200 to 700 m/s). The pellet speed is the key parameter in determining the response time of a shattered pellet system to a plasma disruption event. The calculated speeds from the code simulations of experiments were typically in excellent agreement with the measured values. With the gas gun simulator validated for many test shots and over a wide range of physical and operating parameters, it is a valuable tool for optimization of the injector design, including the fast valve design (orifice size and volume) for any operating pressure (~40 bar expected for the ITER application) and barrel length for any pellet size (mass, diameter, and length). Key design parameters and proposed values for the pellet injectors for the ITER disruption mitigation systems are discussed.

Combs, Stephen Kirk↗

DOE OSTI2013

TESTING MODELS OF INTRINSIC BRIGHTNESS VARIATIONS IN TYPE Ia SUPERNOVAE AND THEIR IMPACT ON MEASURING COSMOLOGICAL PARAMETERS

For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference (B - V - c) between the true B - V color and the fitted color (c) from the SALT-II light curve model, and photometric redshift residual. The data sample includes 251 ugriz light curves from the three-season Sloan Digital Sky Survey-II and 191 griz light curves from the Supernova Legacy Survey 3 year data release. We find that the simplest model of a wavelength-independent (coherent) scatter is not adequate, and that to describe the data the intrinsic-scatter model must have wavelength-dependent variations resulting in a {approx}0.02 mag scatter in B - V - c. Relatively weak constraints are obtained on the nature of intrinsic scatter because a variety of different models can reasonably describe this photometric data sample. We use Monte Carlo simulations to examine the standard approach of adding a coherent-scatter term in quadrature to the distance-modulus uncertainty in order to bring the reduced {chi}{sup 2} to unity when fitting a Hubble diagram. If the light curve fits include model uncertainties with the correct wavelength dependence of the scatter, we find that this approach is valid and that the bias on the dark energy equation-of-state parameter w is much smaller ({approx}0.001) than current systematic uncertainties. However, incorrect model uncertainties can lead to a significant bias on the distance moduli, with up to {approx}0.05 mag redshift-dependent variation. This bias is roughly reduced in half after applying a Malmquist bias correction. For the recent SNLS3 cosmology results, we estimate that this effect introduces an additional systematic uncertainty on w of {approx}0.02, well below the total uncertainty. This uncertainty depends on the choice of viable scatter models and the choice of supernova (SN) samples, and thus this small w-uncertainty is not guaranteed in future cosmology results. For example, the w-uncertainty for SDSS+SNLS (dropping the nearby SNe) increases to {approx}0.04.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

EVOLUTION, NUCLEOSYNTHESIS, AND YIELDS OF AGB STARS AT DIFFERENT METALLICITIES. III. INTERMEDIATE-MASS MODELS, REVISED LOW-MASS MODELS, AND THE pH-FRUITY INTERFACE

We present a new set of models for intermediate-mass asymptotic giant branch (AGB) stars (4.0, 5.0, and 6.0 M{sub ⊙}) at different metallicities (−2.15 ≤ [Fe/H] ≤ +0.15). This set integrates the existing models for low-mass AGB stars (1.3 ≤ M/M{sub ⊙} ≤ 3.0) already included in the FRUITY database. We describe the physical and chemical evolution of the computed models from the main sequence up to the end of the AGB phase. Due to less efficient third dredge up episodes, models with large core masses show modest surface enhancements. This effect is due to the fact that the interpulse phases are short and, therefore, thermal pulses (TPs) are weak. Moreover, the high temperature at the base of the convective envelope prevents it from deeply penetrating the underlying radiative layers. Depending on the initial stellar mass, the heavy element nucleosynthesis is dominated by different neutron sources. In particular, the s-process distributions of the more massive models are dominated by the {sup 22}Ne(α,n){sup 25}Mg reaction, which is efficiently activated during TPs. At low metallicities, our models undergo hot bottom burning and hot third dredge up. We compare our theoretical final core masses to available white dwarf observations. Moreover, we quantify the influence intermediate-mass models have on the carbon star luminosity function. Finally, we present the upgrade of the FRUITY web interface, which now also includes the physical quantities of the TP-AGB phase for all of the models included in the database (ph-FRUITY)

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Calculations of inflaton decays and reheating: with applications to no-scale inflation models

We discuss inflaton decays and reheating in no-scale Starobinsky-like models of inflation, calculating the effective equation-of-state parameter, w, during the epoch of inflaton decay, the reheating temperature, T{sub reh}, and the number of inflationary e-folds, N{sub ∗}, comparing analytical approximations with numerical calculations. We then illustrate these results with applications to models based on no-scale supergravity and motivated by generic string compactifications, including scenarios where the inflaton is identified as an untwisted-sector matter field with direct Yukawa couplings to MSSM fields, and where the inflaton decays via gravitational-strength interactions. Finally, we use our results to discuss the constraints on these models imposed by present measurements of the scalar spectral index n{sub s} and the tensor-to-scalar perturbation ratio r, converting them into constraints on N{sub ∗}, the inflaton decay rate and other parameters of specific no-scale inflationary models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

A marked correlation function for constraining modified gravity models

Future large scale structure surveys will provide increasingly tight constraints on our cosmological model. These surveys will report results on the distance scale and growth rate of perturbations through measurements of Baryon Acoustic Oscillations and Redshift-Space Distortions. It is interesting to ask: what further analyses should become routine, so as to test as-yet-unknown models of cosmic acceleration? Models which aim to explain the accelerated expansion rate of the Universe by modifications to General Relativity often invoke screening mechanisms which can imprint a non-standard density dependence on their predictions. This suggests density-dependent clustering as a `generic' constraint. This paper argues that a density-marked correlation function provides a density-dependent statistic which is easy to compute and report and requires minimal additional infrastructure beyond what is routinely available to such survey analyses. We give one realization of this idea and study it using low order perturbation theory. We encourage groups developing modified gravity theories to see whether such statistics provide discriminatory power for their models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. In conclusion, correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the influence of conformational uncertainty in biomolecular solvation

Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. We also propose a method to increase the sparsity of the gPC expansion by defining a set of conformational “active space” random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance of the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Further more, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.

coarse graining↗

DOE OSTI2015

Improving Power System Modeling. A Tool to Link Capacity Expansion and Production Cost Models

Capacity expansion models (CEM) provide a high-level long-term view at the prospects of the evolving power system. In simulating the possibilities of long-term capacity expansion, it is important to maintain the viability of power system operation in the short-term (daily, hourly and sub-hourly) scales. Production-cost models (PCM) simulate routine power system operation on these shorter time scales using detailed load, transmission and generation fleet data by minimizing production costs and following reliability requirements. When based on CEM 'predictions' about generating unit retirements and buildup, PCM provide more detailed simulation for the short-term system operation and, consequently, may confirm the validity of capacity expansion predictions. Further, production cost model simulations of a system that is based on capacity expansion model solution are 'evolutionary' sound: the generator mix is the result of logical sequence of unit retirement and buildup resulting from policy and incentives. The above has motivated us to bridge CEM with PCM by building a capacity expansion - to - production cost model Linking Tool (CEPCoLT). The Linking Tool is built to onset capacity expansion model prescriptions onto production cost model inputs. NREL's ReEDS and Energy Examplar's PLEXOS are the capacity expansion and the production cost models, respectively. Via the Linking Tool, PLEXOS provides details of operation for the regionally-defined ReEDS scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision-makers. With the recent surge in variable renewable energy (VRE) generators — primarily wind and solar photovoltaics — the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. This report summarizes the analyses and model experiments that were conducted as part of two workshops on modeling VRE for national-scale capacity expansion models. It discusses the various methods for treating VRE among four modeling teams from the Electric Power Research Institute (EPRI), the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and the National Renewable Energy Laboratory (NREL). The report reviews the findings from the two workshops and emphasizes the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making. This research is intended to inform the energy modeling community on the modeling of variable renewable resources, and is not intended to advocate for or against any particular energy technologies, resources, or policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗

DOE OSTI2025

Bayesian Calibration of Nuclear Graphite Property Models Accounting for Model Inadequacy and Impacts on Component Performance

Nuclear-grade structural graphite is extensively utilized in the core designs of various advanced nuclear reactors. In the reactor environment, graphite is subjected to prolonged exposure to extreme conditions, including high temperatures, radiation, and potentially molten salt and oxygen. Such exposure can induce several degradation mechanisms in graphite, including nonuniform volumetric strains caused by irradiation and thermal expansion, leading to stresses that may compromise the performance of graphite components. Assessing component integrity requires accurate models of graphite's thermomechanical response. This report documents the Bayesian calibration of thermomechanical properties for nuclear-grade graphite and their application to graphite component modeling and simulation using the Grizzly code. As part of this work, uncertainty-quantified models were developed for the elastic modulus, coefficient of thermal expansion, irradiation-induced dimensional change, and irradiation-induced creep for graphite grades IG-110, NBG-18, NBG-17, PCEA, and 2114. Using a hierarchical Bayesian approach, multiple experimental data sources were combined to develop Gaussian process models for the properties. Using the Kennedy O'Hagan framework, the uncertainties due to inadequacies in the model and the inherent spread in the experimental data were quantified for three different models. These uncertainty-quantified models, with a model-form correction, were subsequently applied to a coupled-physics simulation of representative graphite components, revealing that the uncertainties have a large impact on the components' deformation.

36 - MATERIALS SCIENCE↗

DOE OSTI2017

Modeling of Hydraulic Fracture Propagation at the kISMET Site Using a Fully Coupled 3D Network-Flow and Quasi- Static Discrete Element Model

Aimed at supporting the design of hydraulic fracturing experiments at the kISMET site, ~1500 m below ground in a deep mine, we performed pre-experimental hydraulic fracturing simulations in order to estimate the breakdown pressure, propagation pressure, fracture geometry, and the magnitude of induced seismicity using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beams between them. A conjugate 3D flow network based on the DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams. In this paper, the 3D hydraulic fracturing model was verified against the analytic solution for a penny-shaped crack model. We applied the model to simulate fracture propagation from a vertical open borehole based on initial estimates of rock mechanical properties and in-situ stress conditions. The breakdown pressure and propagation pressure are directly obtained from the simulation. In addition, the released elastic strain energies of individual fracturing events were calculated and used as a conservative estimate for the magnitudes of the potential induced seismic activities associated with fracturing. The comparisons between model predictions and experimental results are still ongoing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2025

Comparative Study of Enhanced Geothermal System Supply Curves Across CONUS from Two Temperature Models Using the Renewable Energy Potential Model (reV)

Enhanced geothermal systems (EGS) have had recent breakthroughs within the geothermal sector. These breakthroughs are reflected in the National Renewable Energy Laboratory (NREL) 2024 Annual Technology Baseline and will result in updated EGS supply curves (i.e., the available resource capacity relative to cost). Our research uses NREL's Renewable Energy Potential (reV) model to compare EGS supply curves across the conterminous United States (CONUS) for two different temperature models: the Stanford temperature model (STM) and the Southern Methodist University temperature model (SMU). The reV model provides the levelized cost of energy (LCOE) at a consistent resolution across CONUS, taking into consideration transmission costs and constraints as well as technical exclusions pertaining to sensitive cultural, ecological, or infrastructure locations. In addition to a countrywide analysis of both models, we also conducted a regional analysis of Texas. We observed the STM had, on average, lower temperatures across different depths, resulting in slightly higher mean and median LCOEs as compared to the SMU temperature model at the same depths. In the regional analysis for Texas, however, when we compared only the common points between the two temperature models, the STM had lower median and mean LCOEs compared to SMU due to higher temperatures at depths greater than 5 km.

enhanced geothermal systems↗

DOE OSTI2024

Quantification of modeling uncertainty in the Rayleigh damping model

Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.

42 ENGINEERING↗

DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

One-dimensional biomass fast pyrolysis model with reaction kinetics integrated in an Aspen Plus Biorefinery Process Model

A biomass fast pyrolysis reactor model with detailed reaction kinetics and one-dimensional fluid dynamics was implemented in an equation-oriented modeling environment (Aspen Custom Modeler). Portions of this work were detailed in previous publications; further modifications have been made here to improve stability and reduce execution time of the model to make it compatible for use in large process flowsheets. The detailed reactor model was integrated into a larger process simulation in Aspen Plus and was stable for different feedstocks over a range of reactor temperatures. Sample results are presented that indicate general agreement with experimental results, but with higher gas losses caused by stripping of the bio-oil by the fluidizing gas in the simulated absorber/condenser. Lastly, this integrated modeling approach can be extended to other well-defined, predictive reactor models for fast pyrolysis, catalytic fast pyrolysis, as well as other processes.

09 BIOMASS FUELS↗

DOE OSTI2015

How does increasing horizontal resolution in a global climate model improve the simulation of aerosol-cloud interactions?

The Community Atmosphere Model Version 5 is run at horizontal grid spacing of 2, 1, 0.5, and 0.25 degrees, with the meteorology nudged towards the Year Of Tropical Convection analysis, and cloud simulators and the collocated A-Train satellite observations are used to explore the resolution dependence of aerosol-cloud interactions. The higher-resolution model produces results that agree better with observations, showing an increase of susceptibility of cloud droplet size, indicating a stronger first aerosol indirect forcing (AIF), and a decrease of susceptibility of precipitation probability, suggesting a weaker second AIF. The resolution sensitivities of AIF are attributed to those of droplet nucleation and precipitation parameterizations. The annual average AIF in the northern hemisphere mid-latitudes (where most anthropogenic emissions occur) in the 0.25° model is reduced by about 1 W m⁻² (-30%) compared to the 2° model, leading to a 0.26 W m⁻² reduction (-15%) in the global annual average AIF.

Ma, Po -Lun↗