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DOE OSTI2024

Distributed strain sensing using Bi-metallic coated fiber optic sensors embedded in stainless steel

Silica fiber optic sensors are resistant to corrosive environments and high temperatures, making them attractive candidates for harsh conditions found in nuclear and aerospace industries. Moreover, fibers can be deployed remotely for continuous measuring of spatially distributed temperatures and strains. Here, this study investigated embedding a Ni/Cu bi-metallic coated fiber in a stainless-steel 316 (SS316) matrix using laser powder bed fusion towards functionalizing metal components for site-specific health monitoring. The embedded fiber was continuously interrogated during controlled heating to 1000°C. The measured fiber strains were similar to the expected differential thermal strains between the fiber and the SS316 matrix, until divergent behavior was observed at temperatures >500°C. No debonding at the matrix–coating–fiber interfaces was observed during microscopy, but significant interactions between the coatings and matrix resulted in diffusion-driven chemistry variations and Kirkendall void formation. Applying the strain-lag theory revealed plastic behavior in the Ni coating at temperatures >500°C, limiting the strain transfer to the fiber at higher temperatures. It was estimated that the elastic modulus in the Ni coating had decreased from ~200 GPa at room temperature to below 40 GPa, starting at 600°C. The low elastic modulus above 600°C is within the margin of what the tangent modulus would be in the case of bilinear isotropic hardening. Regardless of the divergent strain transfer at higher temperatures, the fiber was exposed to the equivalent of 1.9 % engineering strain at 1000°C, but measured only a 0.7 % engineering strain due to the poor strain transfer. Although compensating for the plastic behavior of Ni proved challenging, the bonding of a brittle silica fiber to a metal matrix surviving to 1000°C invites potential iterations on coating material for future application. For example, the embedded fiber is sufficient for acoustic energy transfer, realizing high temperature distributed acoustic sensing.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Assessing the Impacts of Hydrologic and Land Use Alterations on Water Temperature in the Farmington River Basin in Connecticut

Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e. seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system that includes varying types of watershed development (e.g. deforestation and reservoirs) that can alter the streams’ hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

Freshwater Ecosystem, Water Temperature, Riparian ↗

DOE OSTI2017

Simulation of Taylor flow evaporation for bubble-pump applications

Single-pressure absorption systems incorporate bubble-pump generators (BPGs) for refrigerant separation and passive fluid circulation. In conventional spot-heated BPGs, heat is transferred over a small area, requiring high source temperatures. Distributed-heated BPGs receive thermal input over most of the component surface, enabling low temperature operation. In this investigation, a Volume-of-Fluid phase-change simulation formulation is developed and validated. Here this approach is applied to the evaporating Taylor flow pattern in distributed-heated BPGs. A 2-D axisymmetric simulation is performed, which yields detailed information about the developing heat transfer and two-phase flow phenomena. Results are used to assess predicted trends and sub-models from a 1-D segmented BPG model. Close agreement is obtained between segmented model and simulation results for bubble rise velocity (5–7% deviation), bubble and slug lengths, void fraction (3%), and hydrodynamic pressure drop (18%). Specifying average Taylor bubble lengths from the simulation as an input to the segmented model reduces hydrodynamic pressure drop deviation to 6%. Simulated flow-evaporation heat transfer coefficients are significantly higher than those predicted using analytic models from the literature. A new flow evaporation heat transfer correlation that accounts for developing slug flow effects is proposed, and yields close agreement with simulation results for heat transfer coefficient (AAD = 11%) and overall heat transfer rate (2%). Overall, this investigation provides validation for a distributed-heated BPG modeling approach, which can enable passive refrigeration for diverse applications.

42 ENGINEERING↗

DOE OSTI2022

Anticipating water distribution service outages from increasing temperatures

With projected temperature increases and extreme events due to climate change for many regions of the world, characterizing the impacts of these emerging hazards on water distribution systems is necessary to identify and prioritize adaptation strategies for ensuring reliability. To aid decision-making, new insights are needed into how water distribution system reliability to climate-driven heat will change, and the proactive maintenance strategies available to combat failures. To this end, we present the model Perses, a framework that joins a water distribution network hydraulic solver with reliability models of physical assets or components to estimate temperature increase-driven failures and resulting service outages in the long term. A theoretical case study is developed using Phoenix, Arizona temperature profiles, a city with extreme temperatures and a rapidly expanding infrastructure. By end-of-century under hotter futures there are projected to be 1%–5% more pump failures, 2%–5% more PVC pipe failures, and 3%–7% more iron pipe failures (RCP 4.5–8.5) than a baseline historical temperature profile. Service outages, which constitute inadequate pressure for domestic and commercial use are projected to increase by 16%–26% above the baseline under maximum temperature conditions. The exceedance of baseline failures, when compounded across a large metro region, reveals potential challenges for budgeting, management, and maintenance. An exploration of the mitigation potential of adaptation strategies shows that expedited repair times are capable of offsetting the additional outages from climate change, but will come with a cost.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Simultaneous optical fibre strain and temperature measurements in a hybrid distributed sensor based on Rayleigh and Raman scattering

A new type of sensor for simultaneous measurements of strain and temperature changes in an optical fibre is proposed. Its operation builds on the use of Raman optical time-domain reflectometry and wavelength-tunable quasi-monochromatic Rayleigh reflectometry implemented using a microelectromechanical filter (MEMS). The sensor configuration includes independent Raman and Rayleigh scattering channels. Our experiments have demonstrated that, at a sensing fibre length near 8 km, spatial resolution of 1 – 2 m, and measurement time of 10 min, the noise level (standard deviation) is 1.1 μϵ (μm m{sup −1}) for the measured tension change (at small temperature deviations) and 0.04 °C for the measured temperature change, which allows for effective sensing of mechanical and temperature influences with improved accuracy. (fibre-optic sensors)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Real-Time Monitoring of Temperature Rises of Energized Transformer Cores With Distributed Optical Fiber Sensors

Real-time temperature mapping that solves local overheating problems is important for obtaining an optimized thermal design for high-efficiency power transformers. Internal temperature monitoring of operating power transformers can also be leveraged for asset monitoring applications targeting at fault detection enabling condition-based maintenance. Transformers present a variety of challenging sensing environments such as high levels of electromagnetic interference and limited space for conventional sensors to operate. Immersion of power transformers in insulation oils for thermal management during operation and the presence of relatively large and time-varying electrical and occasional magnetic fields make sensing technologies requiring electrical wires or active power at sensing locations highly undesirable. In this work, we investigate thermal response of a standard telecom fiber instrumented on compact transformer cores by using an optical frequency-domain reflectometry scheme. As a result, correlation between conventional temperature sensing methods and fiber-optic sensing results as well as tradeoffs between spatial resolution and temperature measurement accuracy is discussed and spatially resolved real-time monitoring of temperatures in energized transformers is experimentally demonstrated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. Herein, this paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. These numbers are consistent with state-of-the-art prediction results published in the literature. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

An Assessment of GCM Performance at a Regional Scale Using a Score-Based Method

A multicriteria score-based method was developed to assess the performances of 18 general circulation models (GCMs) in the study region from 1970 to 2005. The results indicate the following. (1) GCMs simulate temperature better than rainfall. The temporal and spatial distributions of simulated temperature performed well compared with those from the observations. In comparison to temperature, the spatial distribution of simulated precipitation performed poorly. Most of the GCMs underestimated temperature and overestimated precipitation. (2) The Grubbs test was used to detect anomalous moving changes in the rank score (RS) results; the inm-cm4 and ipsl-cm5b-lr models were rejected when simulating temperature, while the bnu-esm and canesm2 models performed poorly when simulating precipitation. (3) Adding or removing any criterion does not significantly influence the RS result, which indicates that the multicriteria score-based method is robust. The advantages of using multicriteria score-based method to assess GCMs performance were demonstrated, and this method also provides a more comprehensive assessment when compared with the single-criterion method. The multicriteria method could replace other criteria as the research requirements and could be easily extended to different study regions; the results could be used for better informed regional climate change impact analyses.

Shi, Fangxin↗

DOE OSTI2016

DIFFUSE AURORA ON GANYMEDE DRIVEN BY ELECTROSTATIC WAVES

The role of electrostatic electron cyclotron harmonic (ECH) waves in producing diffuse auroral emission O i 1356 Å on Ganymede is investigated. Electron precipitation flux entering the atmosphere of Ganymede due to pitch-angle diffusion by ECH waves into the atmospheric loss-cone is calculated. The analytical yield spectrum approach for electron energy degradation in gases is used for calculating diffuse auroral intensities. It is found that calculated O i 1356 Å intensity resulting from the precipitation of magnetospheric electrons observed near Ganymede is insufficient to account for the observed diffuse auroral intensity. This is in agreement with estimates made in earlier works. Heating and acceleration of ambient electrons by ECH wave turbulence near the magnetic equator on the field line connecting Ganymede and Jupiter are considered. Two electron distribution functions are used to simulate the heating effect by ECH waves. Use of a Maxwellian distribution with temperature 100 eV can produce about 50–70 Rayleigh O i 1356 Å intensities, and the kappa distribution with characteristic energy 50 eV also gives rise to intensities with similar magnitude. Numerical experiments are performed to study the effect of ECH wave spectral intensity profile, ECH wave amplitude, and temperature/characteristic energy of electron distribution functions on the calculated diffuse auroral intensities. The proposed missions, joint NASA/ESA Jupiter Icy Moon Explorer and the present JUNO mission to Jupiter, would provide new data to constrain the ECH wave and other physical parameters near Ganymede. These should help confirm the findings of the present study.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Impact of the Annealing Temperature on the Local Se Distribution and Optical Properties in CdSexTe1-x Solar Cells by Multimodal X-ray Microscopy

The introduction of Se in CdTe solar-cell absorbers has led to multiple beneficial effects in the devices, including an increased short-circuit current and an extended charge-carrier lifetime. A critical manufacturing step of CdSexTe1-x solar cells is CdCl2 annealing at high temperatures, during which Se diffuses through the absorber layer. Understanding the local Se distribution in the absorber and its impact on optical properties of the material is key for the further development of this thin-film solar cell technology. In a multimodal x-ray scanning microscopy approach, we investigated the impact of the annealing temperature of CdSexTe1-x solar cells on the Se distribution and on optical characteristics of the material. For this purpose, we exploited an upgraded x-ray excited optical luminescence (XEOL) detection unit that enables simultaneous spectrally and temporally resolved XEOL mapping at synchrotron facilities that are ideal for studying thin-film solar-cell materials with hard x-rays featuring high penetration depth and lateral resolution.

14 SOLAR ENERGY↗

DOE OSTI2024

Review and meta-analysis of electron temperatures from high-intensity laser–solid interactions

The accelerated electron spectrum from high-intensity laser–solid interaction is often conveniently described using a Boltzmann distribution, whose temperature is known within the field as the hot-electron temperature. The importance of the electron temperature is highlighted by the sheer number of experimental and simulation studies on the subject over the past three decades. Recently, multi-kJ, multi-ps pulses have yielded electron spectra with temperatures far beyond the expected ponderomotive result. Expressions that predict the electron temperature considering laser parameters beyond intensity and wavelength have been developed, albeit using small datasets. In this review, we present what is, to the best of our knowledge, the largest dataset of electron temperatures gathered from experimental measurements and particle-in-cell simulations. This dataset allows us to compare existing analytical and empirical hot-electron temperature scaling models over a wide parameter range. We also develop new scaling models that incorporate the laser pulse duration of the laser and the plasma scale length. Three models that include pulse-duration and scale length dependence are especially successful at predicting both simulated and experimental data. The dataset will soon be made publicly available to encourage further investigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Numerical Analysis of the Effect of Infrared Radiation on Cryogenic Inertial Confinement Fusion Targets

The present work applies the finite element method to calculate the maximum allowable time that cryogenic inertial confinement fusion (ICF) targets can be exposed to infrared radiation (IR). Hence, a 3-D numerical model integrated with discrete coordinate radiation model was developed to investigate the influence of transmittance of the laser entrance holes (LEHs) and boundary conditions on the temperature field distribution and the maximum DT layer deterioration time for CH, Be, and diamond capsules. Our study shows that introducing such a radiation model can accurately obtain more detailed spatial and temporal distribution information in the ICF targets. The simulation results demonstrate that the Be and diamond capsules provided much better temperature field homogenization than the CH capsule under equivalent boundary conditions, but the CH capsule was heated more by IR radiation than the Be and diamond. In addition, the maximum DT layer deterioration time was significantly increased to 3 s when decreasing the transmittance of the LEH from 0.2 to 0.01. However, either reducing the capsule IR absorption or increasing the inner hohlraum IR absorption demonstrated no conclusive increase in the maximum DT layer deterioration time. These results are expected to provide useful parameters in the design of cryogenic targets and shroud systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Random-Cluster Correlation Inequalities for Gibbs Fields

In this note we prove a correlation inequality for local variables of a Gibbs field based on the connectivity in a random cluster representation of the non overlap configuration distribution of two independent copies of the field. As a consequence, we show that absence of a particular type of percolation (of Machta–Newman–Stein blue bonds) implies uniqueness of Gibbs distribution in EA Spin Glasses. In dimension two this could constitute a step towards a proof that the critical temperature is zero.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Flue Gas CO 2 Capture via Electrochemically Mediated Amine Regeneration: Desorption Unit Design and Analysis

We analyzed the performance of an electrochemical desorption unit to be implemented in an electrochemically mediated amine regeneration (EMAR) process, which separates CO 2 and regenerates the amine upon modulation of a targeted species that complexes with the amine molecules. Using a simplified one-dimensional boundary layer model, we combined the kinetic expressions of the faradaic reactions with mass transfer effects to analyze the polarization behavior of the flow cell. Mass transfer limitations become dominant at high current densities, leading to increases in energy consumption. An understanding of scaling laws with key parameters, such as the Sherwood number, is required to achieve high power operation with minimal mass transfer losses. Because the EMAR chemistry is sensitive to temperature, heat distribution in the unit is also examined. Our results indicate that the desorption performance increases with addition of a high-temperature heat source. Additionally, incorporation of membrane crossover suggests energy losses because of diffusion and migration of active species. Furthermore, these findings inform on scaling laws for at-scale desorber design and provide a model for scaling up the EMAR system for larger scale operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Ultrasonic Measurements of Temperature Profile and Heat Fluxes in Coal-Fired Power Plants (Final Report)

Many industrial processes are inaccessible or inhospitable to characterization by traditional temperature measurement methods, such as thermocouples, especially over prolonged exposure to harsh environments. Ultrasound is an established characterization technology with diverse applications ranging from medical imaging to therapies to flaw detection to nondestructive evaluation. Ultrasound may characterize solid materials and components noninvasively as a nondestructive evaluation modality and obtain internal measurements of material properties. For example, the speed of ultrasound propagation changes with Young’s modulus and Poisson’s ratio, which can be found from its measurements. Traditional ultrasonic characterization assumes all material properties remain constant with the position. When this assumption holds, a property of interest may be measured by relating it to the speed of ultrasound propagation (or a speed of sound, SOS) and measuring the SOS by timing the ultrasound propagation through a known distance. However, when a property of interest is spatially distributed, the propagation time depends on the SOS changing with the position along the ultrasound propagation path. The multiple temperature distributions may lead to an identical time of flight (TOF). Temperature is one property that impacts the speed of ultrasound and often cannot be assumed to remain constant with the position. Previously, in the context of temperature, we addressed the challenge of ultrasonic characterization of spatially distributed properties by developing a method for measuring segmental temperature distributions (MSTD). This method divides the ultrasonic propagation into segments bound by echogenic features. These features provide ultrasonic interfaces where some energy is reflected toward the receiving transducer, and the rest continues through the medium. The time-of-flight between the echoes reflected from echogenic features characterizes the spatial distribution in the properties of interest in the corresponding segment of the ultrasonic propagation path. This project demonstrated the application of the MSTD method in industrial conditions of the coal-fired power plant. We implemented the MSTD using metals and alloys waveguides, which may be the existing structure for which the temperature distribution is characterized or purposefully designed waveguides added to the structure by welding or other means specifically to quantify thermal properties using the MSTD method. Previous iterations of the MSTD method used ceramic and cementitious waveguides, which significantly attenuate ultrasound. On the other hand, low attenuation in metallic waveguides creates interactions between echogenic features which compilates the signal analysis in the segmental TOF measurements. We have established the WG design principles that minimize the interferences between trailing and primary echoes and, in some cases, eliminate them. The waveguides in which echoes do not interfere improve the timing accuracy and the robustness of ultrasonic measurements of the spatial distributions in material properties. Our emphasis remained on the estimation of the temperature distributions. We have developed general recommendations for designing ultrasonically segmented waveguides with the reduced influence of trailing echoes. Two of our waveguide designs were tested in the industry. The first waveguide was designed for insertion into a combustion zone of the utility-scale coal-fired power plant boiler. The second design allows the characterization of temperature distribution in the direction normal to the boiler’s water wall, a large heat exchanger converting the chemical energy released during combustion to the steam driving the electrical power generation turbines. These waveguides were designed to operate within a restrictive space of thermally insulated water wall and incorporate densely located echogenic features while combatting the influence of trailing echoes. The project has successfully demonstrated the feasibility of using the developed method for accurate, continuous, and robust temperature measurements in extreme environments of power generation and other industrial processes. It, therefore, has achieved its overarching goal of advancing the technology readiness level of the novel Ultrasound Measurements of Segmental Temperature Distribution (US-MSTD) method for real-time measurements of the temperature distribution and heat fluxes closer to commercial availability, developing a prototype multipoint measurement system, and validating its performance on coal-fired utility boilers. The success of this project was achieved in collaboration with the power generator, Rocky Mountain Power, and set the stage for the transfer of this technology from the laboratory to the industry.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2017

Numerical investigation of a joint approach to thermal energy storage and compressed air energy storage in aquifers

Different from conventional compressed air energy storage (CAES) systems, the advanced adiabatic compressed air energy storage (AA-CAES) system can store the compression heat which can be used to reheat air during the electricity generation stage. Thus, AA-CAES system can achieve a higher energy storage efficiency. Similar to the AA-CAES system, a compressed air energy storage in aquifers (CAESA) system, which is integrated with an aquifer thermal energy storage (ATES) could possibly achieve the same objective. In order to investigate the impact of ATES on the performance of CAESA, different injection air temperature schemes are designed and analyzed by using numerical simulations. Key parameters relative to energy recovery efficiencies of the different injection schemes, such as pressure distribution and temperature variation within the aquifers as well as energy flow rate in the injection well, are also investigated in this study. The simulations show that, although different injection schemes have a similar overall energy recovery efficiency (~97%) as well as a thermal energy recovery efficiency (~79.2%), the higher injection air temperature has a higher energy storage capability. Our results show the total energy storage for the injection air temperature at 80 °C is about 10% greater than the base model scheme at 40 °C. Sensitivity analysis reveal that permeability of the reservoir boundary could have significant impact on the system performance. However, other hydrodynamic and thermodynamic properties, such as the storage reservoir permeability, thermal conductivity, rock grain specific heat and rock grain density, have little impact on storage capability and the energy flow rate. Our study suggests that the combination of ATES and CAESA can help keep the high efficiency of energy storage so as to make CAESA system more efficiency.

25 ENERGY STORAGE↗

DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗