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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Abstract Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.

25 ENERGY STORAGE↗
DOE OSTI2024

Global Corn Heat Stress: Mean and SD of Degree Days Above 29°C based on NEX-GDDP-CMIP6 Climate Projections

Description This global dataset provides the estimated mean and standard deviation (SD) of corn heat stress (degree days above 29°C) for a set of climate models in NEX-GDDP-CMIP6 at 0.25-degree resolution. The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). The current dataset includes: Long-Term Average Degree Days Above 29°C- Historical Long-Term Average Degree Days Above 29°C- SSP245 Long-Term Standard Deviation of Degree Days Above 29°C- Historical Long-Term Standard Deviation of Degree Days Above 29°C- SSP245 The mean and SD are calculated over 1985-2014 for the historical period and over 2035-2064 for future projections. A full description of methods, including growing season, daily temperature distribution, and statistical coefficients, can be found in Haqiqi (2024). The source climate data are obtained from https://ds.nccs.nasa.gov/thredds2/catalog/catalog.html and are described in Thrasher et al (2022). The codes used to create this dataset are available at https://github.com/ihaqiqi/dd29c_nex_cmip6. Acknowledgments This work was supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. The data processing, computation, and storage were completed on Purdue Anvil supercomputer and cyberinfrastructure supported by the National Science Foundation HDR award # 2118329: "NSF Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE)". References Haqiqi. I. (2024). Trade can buffer climate-induced risks and volatilities in crop supply. Environmental Research: Food Systems. https://doi.org/10.1088/2976-601X/ad7d12 Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific Data, 9(1), 262. https://doi.org/10.1038/s41597-022-01393-4

Climate Change↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2021

Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

14 SOLAR ENERGY↗
DOE OSTI2024

Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle: High-Resolution Sampling Every 4 Hours Over 22 Days

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Zero-day Attack Detection in Digital Substations Using In-Context Learning

In this paper, we address the critical challenge of detecting zero-day attacks in digital substations that employ the IEC-61850 communication protocol to ensure the security and reliability of modern power systems. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to unknown or zero-day attacks remains a challenge. We propose an approach that leverages the in-context learning ability of transformer architecture, which enables the model to learn from a few examples of a new task without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than 87% detection accuracy on zero-day attacks while the existing baselines fail. We believe this work has the potential to enhance the security of digital substations by enabling the effective detection of zero-day attacks.

LIu, Chen-Ching [Virginia Tech] (ORCID:00000002894↗
DOE OSTI2021

Two-Day Westward-Propagating Inertio-Gravity Waves during GoAmazon

The dynamical and thermodynamical features of Amazonian 2-day westward-propagating inertia–gravity (WIG) waves are examined. On the basis of a linear regression analysis of satellite brightness temperature and data from the 2014–15 Observations and Modeling of the Green Ocean Amazon (GoAmazon) field campaign, it is shown that Amazonian WIG waves exhibit structure and propagation characteristics consistent with the n = 1 WIG waves from shallow water theory. These WIG waves exhibit a pronounced seasonality, with peak activity occurring from March to May and a minimum occurring from June to September. Evidence is shown that mesoscale convective systems over the Amazon are frequently organized in 2-day WIG waves. Results suggest that many of the Amazonian WIG waves come from preexisting 2-day waves over the Atlantic, which slow down when coupled with the deeper, more intense convection over tropical South America. In contrast to WIG waves that occur over the ocean, Amazonian 2-day WIG waves exhibit a pronounced signature in surface temperature, moisture, and heat fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

2023 Sandia Day at UT Austin

On Wednesday, March 8 th and Thursday, March 9 th , 2023, the University of Texas at Austin hosted Sandia National Laboratories (Sandia) for “Sandia Day 2023 at UT Austin” with the intention of reviewing, planning and shaping ongoing and future collaborations in key areas that reflect each organization’s priorities and strengths. The event brought together nearly 100 UT and Sandia participants including executive leadership, researchers, faculty, staff, and students. The primary sessions of Sandia Day consisted of a half-day tour of select J.J. Pickle Research Campus facilities, a networking happy hour, leadership meetings, presentations by both Sandia and UT Austin representatives in areas of research strategic priorities: Grid Resiliency, Examining Climate Change, and Microelectronics, and a research poster session with lunch. The group also discussed growth opportunities in the following research areas: nuclear and radiation engineering, pulsed power and fusion physics, and digital engineering, specifically as it related to materials discovery and advanced manufacturing. Appendix A contains the full Sandia Day agenda.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Day-ahead continuous double auction-based peer-to-peer energy trading platform incorporating trading losses and network utilisation fee

Integration of distributed energy resources, such as photovoltaic solar (PV), introduces new opportunities to establish local energy market frameworks to improve renewable energy utilisation in residential sectors. Such peer-to-peer (P2P) energy trading refers to a local market structure where customers (and prosumers) interact to share excess PV generation to enhance the individual and community social welfare. In this work, a day-ahead continuous double auction (CDA)-based P2P market structure considering network losses and network utilisation fees was designed. Day-ahead PV energy is modelled using fractional integral polynomials and the output is forecasted using an autoregressive integrated moving average model for each market interval. Based on the customer load and excess PV energy, the CDA market is cleared using a bid/ask matching mechanism. The performance of the P2P market was evaluated by computing different welfare metrics while analysing the effect of network constraints. The results show that the designed CDA-based P2P market structure increases the social welfare of all participants by an average of 17.75% compared to the baseline for the presented cases. Moreover, the impact of the forecasting error between the day-ahead and real-time market was also quantified.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗
DOE OSTI2025

Homoeolog expression divergence contributes to time of day changes in transcriptomic and glucosinolate responses to prolonged water limitation in Brassica napus

SUMMARY Water availability is a major determinant of crop production, and rising temperatures from climate change are leading to more extreme droughts. To combat the effects of climate change on crop yields, we need to develop varieties that are more tolerant to water‐limited conditions. We aimed to determine how diverse crop types (winter/spring oilseed, tuberous, and leafy) of the allopolyploid Brassica napus , a species that contains the economically important rapeseed oilseed crop, respond to prolonged water limitation. We exposed plants to an 80% reduction in water and assessed growth and color on a high‐throughput phenotyping system over 4 weeks and ended the experiment with tissue collection for a time course transcriptomic study. We found an overall reduction in growth across cultivars but to varying degrees. Diel transcriptome analyses revealed significant accession‐specific changes in time‐of‐day regulation of photosynthesis, carbohydrate metabolism, and sulfur metabolism. Interestingly, there was extensive variation in which homoeologs from the two parental subgenomes responded to water limitation across crop types that could be due to differences in regulatory regions in these allopolyploid lines. Follow‐up experiments on select cultivars confirmed that plants maintained photosynthetic health during the prolonged water limitation while slowing growth. In two cultivars examined, we found significant time of day changes in levels of glucosinolates, sulfur‐ and nitrogen ‐rich specialized metabolites, consistent with the diel transcriptomic responses. These results suggest that these lines are adjusting their sulfur and nitrogen stores under water‐limited conditions through distinct time of day regulation.

Ricono, Angela [University of Minnesota St. Paul M↗
DOE OSTI2022

Seven days of ischemic preconditioning augments hypoxic exercise ventilation and muscle oxygenation in recreationally trained males

This investigation sought to assess whether single or repeated bouts of ischemic preconditioning (IPC) could improve oxyhemoglobin saturation ([Formula: see text]) and/or attenuate reductions in muscle tissue saturation index (TSI) during submaximal hypoxic exercise. Fifteen healthy young men completed submaximal graded exercise under four experimental conditions: 1) normoxia (NORM), 2) hypoxia (HYP) [oxygen fraction of inspired air ([Formula: see text]) = 0.14, ∼3,200 m], 3) hypoxia preceded by a single session of IPC (IPC1-HYP), and 4) hypoxia preceded by seven sessions of IPC, one a day for 7 consecutive days (IPC7-HYP). IPC7-HYP heightened minute ventilation (V̇e) at 80% HYP peak cycling power output ( W peak ) (+10.47 ± 3.35 L·min −1 , P = 0.006), compared with HYP, as a function of increased breathing frequency. Both IPC1-HYP (+0.17 ± 0.04 L·min −1 , P < 0.001) and IPC7-HYP (+0.16 ± 0.04 L·min −1 , P < 0.001) elicited greater oxygen consumption (V̇o 2 ) across exercise intensities compared with NORM, whereas V̇o 2 was unchanged with HYP alone. [Formula: see text] was unchanged by either IPC condition at any exercise intensity, yet the reduction of muscle TSI during resting hypoxic exposure was attenuated by IPC7-HYP (+9.9 ± 3.6%, P = 0.040) compared with HYP, likely as a function of reduced local oxygen extraction. Considering all exercise intensities, IPC7-HYP attenuated reductions of TSI with HYP (+6.4 ± 1.8%, P = 0.001). Seven days of IPC heightens ventilation, posing a threat to ventilatory efficiency, during high-intensity submaximal hypoxic exercise and attenuates reductions in hypoxic resting and exercise muscle oxygenation in healthy young men. A single session of IPC may be capable of modulating hypoxic ventilation; however, our present population was unable to demonstrate this with certainty.

Physiology↗
DOE OSTI2023

GEFSv12 High- and Low-Skill Day-10 Tornado Forecasts

Abstract On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes because of their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the United States were exploited while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). There were 88 high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden–Julian oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific decadal oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 overforecast the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters. Significance Statement This research provides a framework for the anticipation of a more (or less) skillful 10-day tornado forecast in an operational numerical weather prediction system. High-skill forecasts were associated with substantial tropical convection and warm sea surface temperature throughout the Pacific Ocean and Gulf of Mexico, whereas the underlying cause of low-skill forecasts were typically associated with a blocking anticyclone over eastern Russia. These findings are important because they permit increased or decreased confidence in a long-range forecast of tornado occurrence based on a dynamical prediction system.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Board 160: Empowerment in STEM Day: Introducing High School Girls to Careers at National Laboratories (Work in Progress)

In the US, women are still vastly underrepresented in STEM (science, technology, engineering, and mathematics) careers, and various studies have shown that girls' interest in STEM careers wane as high school progresses. With this challenge in mind, Empowerment in STEM Day was organized by Lawrence Berkeley National Laboratory (LBNL), hosting 47 high school students from 6 public high schools in the area. This one-day event was designed and executed through a collaboration between the Women's Support and Empowerment Council (WSEC) and the K-12 STEM Education and Outreach Program at LBNL. The main goal of this program was to provide high school girls, who have little access to STEM career role-models in their immediate surroundings with insights into how a career in STEM looks like. Invitations to participate in the program were sent out to six local high schools in the Bay area asking educators to identify female students that were interested in STEM. Each high school participant was provided with an opportunity to experience a national laboratory environment, learn more about summer workshops and paid summer research internship opportunities at LBNL for high school students, and engage directly with LBNL's employees through job shadow, career mapping and speed networking sessions. In this paper, we will present an overview of the event organization, challenges faced during planning and execution of the event, discuss the lessons learned from the first Empowerment in STEM Day and suggest strategies for incorporating such events at other national laboratories and academic institutions as part of a vital effort into recruiting and retaining more high school girls in STEM-based careers. Additionally, since this was the first in-person event hosted by LBNL's K-12 Program after the pandemic, we will also share the strategies implemented at the event so as to engage both remote and on-site employees as volunteers.

Bose, Baishakhi↗
DOE OSTI2022

High Performance Flow Battery with Inexpensive Inorganic Reactants: DAYS Program (Final Technical Report)

RTRC’s DAYS projects focused on new flow battery (FB) chemistries for long duration energy storage: one that pairs aqueous polysulfide and manganese active materials in strongly alkaline electrolytes (S/Mn); another explores ligand-modified metal (LMM) chemistries in moderate pH electrolyte conditions. The S/Mn chemistry met durability targets by running for over 500 cycles and 1000 hours. Combined with this durability, power densities of up to 60 mW/cm2 at 80% round-trip efficiency, and exceptionally low-cost S and Mn active materials, the S/Mn FB offers a pathway to meeting the <5¢/kW-hr levelized cost of storage (LCOS) target first proposed by the DAYS Program and accepted by the DOE Energy Storage Grand Challenge. The LMM chemistry made impressive progress as well, including durability tests that retained performance over 600 hours of cycling. Cell optimization allowed this chemistry to operate as high as 500 mW/cm2 at 80% round-trip efficiency, nearly 10x the power density of S/Mn and comparable to the power density achieved in RTRC’s flow battery stacks using acidic vanadium battery solutions. This enabled the LMM chemistry to also offer a pathway to the <5¢/kW-hr LCOS DAYS and DOE targets. The promising results from the LMM chemistry have led to a recent DOE AMO award to advance the technology to a 5 kW, 20 kW-hr prototype.

25 ENERGY STORAGE↗
DOE OSTI2022

K2-79b and K2-222b: Mass Measurements of Two Small Exoplanets with Periods beyond 10 days that Overlap with Periodic Magnetic Activity Signals

We present mass and radius measurements of K2-79b and K2-222b, two transiting exoplanets orbiting active G-type stars observed with HARPS-N and K2. Their respective 10.99 day and 15.39 day orbital periods fall near periods of signals induced by stellar magnetic activity. The two signals might therefore interfere and lead to an inaccurate estimate of exoplanet mass. We present a method to mitigate these effects when radial velocity (RV) and activityindicator observations are available over multiple observing seasons and the orbital period of the exoplanet is known. We perform correlation and periodogram analyses on subsets composed of each target's two observing seasons, in addition to the full data sets. For both targets, these analyses reveal an optimal season with little to no interference at the orbital period of the known exoplanet. We make a confident mass detection of each exoplanet by confirming agreement between fits to the full RV set and the optimal season. For K2-79b, we measure a mass of 11.8 ± 3.6 M ⊕ and a radius of 4.09 ± 0.17 R ⊕ . For K2-222b, we measure a mass of 8.0 ± 1.8 M ⊕ and a radius of 2.35 ± 0.08 R ⊕ . According to model predictions, K2-79b is a highly irradiated Uranus analog and K2-222b hosts significant amounts of water ice. We also present a RV solution for a candidate second companion orbiting K2-222 at 147.5 days.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Present-day correlations are insufficient to predict cloud albedo change by anthropogenic aerosols in E3SM v2

Cloud albedo susceptibility to droplet number perturbation remains a source of uncertainty in understanding aerosol–cloud interactions and thus both past and present climate states. Through the Energy Exascale Earth System Model (E3SM) v2 experiments, we probe the effects of competing processes on cloud albedo susceptibility of low-lying marine stratocumulus in the northeast Pacific. In present-day conditions, we find that increasing precipitation suppression by aerosols increases cloud albedo susceptibility, whereas increasing cloud sedimentation decreases it. By constructing a hypothetical model configuration exhibiting negative susceptibility under all conditions, we conclude that cloud albedo change due to aerosol perturbation cannot be predicted by present-day co-variabilities in E3SM v2. As such, our null result herein challenges the assumption that present-day climate observations are sufficient to constrain past states, at least in the context of cloud albedo changes to aerosol perturbation.

54 ENVIRONMENTAL SCIENCES↗