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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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DOE OSTI2022

Factors Affecting Override Behavior during Demand Flexibility from High-Resolution Smart Thermostat Data

In this study, we delineate key weather and demographic predictors of override behavior in residential buildings during connected thermostats demand response events. Anticipating and reducing overrides is critical to demand flexibility (DF) program success. We use high- dimensional fixed effects linear regression techniques on a large ecobee dataset for about 5,000 enrolled households in the United States. We identify critical weather (indoor and outdoor temperature), building (housing type), and occupant (previous DF overrides and previous DF event exposure) factors influencing the override patterns of customers during thermostat demand response. We also differentiate DF events by season to understand the different indoor and outdoor conditions that might influence seasonal override rates. We found significant differences in override rates between building types, with single-family semi-detached homes generally having the highest overrides. Having a history of overrides was additionally a critical factor in predicting occupant response to future DF events. Understanding these override differences is necessary for rural electric cooperatives and emerging DF programs without access to large DR historical data. Overall, we provide critical information on technical and local demographic characteristics that may be correlated to building type to influence strategies to reduce overrides and improve the adoption of DF technologies.

connected thermostats (CTs)↗
DOE OSTI2024

Evaluating Ensemble Predictions of South Asian Monsoon Low Pressure System Genesis

Abstract Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.

Suhas, D. L.↗
DOE OSTI2025

Draft Prototype Microreactor Transportation Safety Program

Microreactors are compact reactors capable of producing less than 50 megawatts of electrical energy. Typically, these reactors are factory-fabricated and designed to be easily transportable by truck, rail, vessel, or air. Microreactor designs often assume that the unit can be transported containing either unirradiated or irradiated fuel. The interest in microreactors is driven by several factors, including the need to generate power on at remote locations, at military installations, at facilities such as data centers, and in areas recovering from natural disasters. The U.S. Department of Defense is actively pursuing the microreactor concept to meet the increasing energy demands of military operations that require portable and dense power sources. Commercial vendors are also exploring microreactor concepts. The report Microreactor Transportation Emergency Planning Challenges (Maheras et al. 2024) outlined the emergency planning challenges associated with the transportation of microreactors by road, rail, and by barge/ship. The successful commercial deployment and redeployment of microreactors will also require the development of microreactor transportation safety programs. The elements in these safety programs are not specific to microreactors; however, the transport of microreactors may pose unique challenges in these areas. This report builds on the report Microreactor Transportation Emergency Planning Challenges (Maheras et al. 2024) and develops the elements of a prototype microreactor transportation safety program that describes the elements that should be contained vendor-developed microreactor transportation safety programs, identifying the unique elements associated with microreactor transport. This will provide vendors and their transportation contractors a basis for their transportation planning and will accelerate the commercial deployment and redeployment of microreactors by identifying those issues unique to microreactor transport. The emphasis of this report is on highway transport of microreactors. This is based on a U.S. Nuclear Regulatory Commission transportation package approval strategy of crawl-walk-run, where transport by highway is evaluated first (Coles et al. 2021, 2024, Maheras et al. 2021), then other surface modes (rail and barge/ship), and finally air transport. Evaluation of maritime transport of microreactors was recently initiated (Rigato et al. 2024, Maheras et al. 2025). The report first discusses microreactors in general and microreactor transportation safety program planning assumptions. The report then provides a description of the transportation safety planning process and provides an extensive discussion of the elements of transportation safety programs. Specific elements examined included transportation roles and responsibilities, transportation planning, transportation mode and route selection, carrier selection, transportation packaging, advance notification of shipments, public information and communications, emergency response plans and procedures, inspections, security, safe parking, shipment tracking, weather and road conditions, medical preparedness, training and exercises, and program evaluation. The report then identifies the unique elements of a transportation safety program associated with microreactor transport. These unique elements were in the areas of: the unusual nature of microreactor designs, compensatory measures, increased radiation dose rates in the vicinity of microreactors, transportation package approval versus 10 CFR 50.59, and the use of a risk-informed transportation package approval process.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Reduced viscosity steadily weakens oceanic currents

The viscosity of both air and water is temperature dependent. A rising temperature leads to an increased viscosity for air but a decreased viscosity for water. As climate becomes warmer, this increased air viscosity can partly inhibit the reduction of wind stress over the ocean, and the reduced water viscosity causes less downward momentum and heat transport. As these opposing effects of warming on air and water viscosity are not included in the state-of-the-art climate models, the understanding of their potential impacts on the response of the climate system to the anthropogenic warming is lacking. Here, via analyzing the Simple Ocean Data Assimilation oceanic reanalysis dataset, we show that the ocean heat content increases at a rate of ~1.3 × 10 22 J/yr over 35 years, which leads to a continuous reduction of oceanic viscosity. As a result, the ocean vertical shear enhances with a shoaling of the mixed layer depth and a reduced vertical linkage in the ocean. Our calculations show a reduction of the oceanic kinetic energy at a rate of ~2.4 × 10 16 J/yr. Potentially, this could generate far-reaching impacts on the energy storage of the climate system and, hence, could pace the global warming. Thus, it is important to include the temperature-dependent viscosity in our climate models. Freshwater discharged from polar ice sheets and mountain glaciers also contributes to the reduction in oceanic viscosity but, at present, to a lesser extent than that in oceanic warming. Reduced oceanic viscosity, therefore, is an important, but hitherto overlooked, response to a warming climate and contributes to many recent weather extremes including heavier rainfall rates in hurricanes, slackening of the polar vortex, and oceanic heat waves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response

controlled islanding↗
DOE OSTI2026

Comparing Synoptic Pattern Evolution for Flash‐Flood‐Producing and Non‐Flash‐Flood‐Producing Mesoscale Convective Systems in the United States

Understanding how the short-term evolution of synoptic weather patterns influence Mesoscale Convective Systems (MCSs) is essential, as these systems are responsible for over half of central U.S. flash floods, leading to substantial socioeconomic and water resource management impacts. This study analyzes long-term MCS data, flash flood reports, and atmospheric reanalyses from 2007 to 2017 using a machine learning clustering algorithm to examine how the synoptic weather patterns evolve prior to MCS initiation. While the clusters reflect seasonal and regional differences in MCS occurrence, they do not consistently distinguish between MCSs that do and do not produce flash floods. Systems in the southern Great Plains are more flood-prone when a synoptic-scale forcing, located near the system, drives strong water vapor transport from the nearby moisture source. More generally under different synoptic weather patterns, a broader precipitating area is the most dominant factor governing MCS flash flood potential.

atmospheric dynamics↗
DOE OSTI2022

Trait drift in microalgae and applications for strain improvement

Microalgae are increasingly used to generate a wide range of commercial products, and there is growing evidence that microalgae-based products can be produced sustainably. However, industrial production of microalgal biomass is not as developed as other biomanufacturing platform technologies. In addition, results of bench-scale research often fail to translate to large-scale or mass production systems. This disconnect may result from trait drift and evolution occurring, through time, in response to unique drivers in each environment, such as cultivation regimes, weather, and pests. Moreover, outdoor and indoor cultivation of microalgae has the potential to impose negative selection pressures, which makes the maintenance of desired traits a challenge. In this context, this review sheds the light on our current understanding of trait drift and evolution in microalgae. Here, we delineate the basics of phenotype plasticity and evolution, with a focus on how microalgae respond under various conditions. In addition, we review techniques that exploit phenotypic plasticity and evolution for strain improvement in view of industrial commercial applications, highlighting associated advantages and shortcomings. Finally, we suggest future research directions and recommendations to overcome unwanted trait drift and evolution in microalgae cultivation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

North‐East Peri‐Tethyan Water Column Deoxygenation and Euxinia at the Paleocene Eocene Thermal Maximum

Abstract The Paleocene–Eocene Thermal Maximum (PETM) is associated with climatic change and biological turnover. It shares features with the Oceanic Anoxic Events (OAEs) of the Mesozoic, such as transient global warming and biogeochemical perturbations. However, the PETM experienced a more muted expansion of marine anoxia compared to the Mesozoic OAEs (especially OAE2), with geographically limited evidence for photic zone euxinia (PZE). We explore the extent and drivers of marine deoxygenation during the PETM using biomarkers for water column euxinia and anoxia as well as an intermediate complexity Earth system model (cGEnIE). These reveal that the water column in the North‐East Peri‐Tethys became anoxic, with euxinic conditions reaching the photic zone (PZE) during the PETM. Our model shows that euxinia developed due to a global increase in the ocean nutrient inventory with concomitant oxygen consumption, similar to findings for OAE2. The particularly strong regional response in the NE Peri‐Tethys appears to arise from a combination of global CO 2 ‐weathering forcing, regionally restricted circulation and upwelling of sulphidic thermocline waters. Unlike OAE2, anoxia and PZE do not become widespread in our PETM simulations, consistent with new and existing geochemical and biological proxy data. This globally muted response could result from reduced biogeochemical feedbacks to climate forcing relative to the mid‐Cretaceous climate. Our observations suggest that similar mechanisms operated in response to disparate Cenozoic (PETM) and Mesozoic (OAEs) transient global warming events, while also highlighting that background conditions are crucial in modulating the sensitivity of Earth's system to them.

Behrooz, L.↗
DOE OSTI2021

Redefining Resource Adequacy for Modern Power Systems: A Report of the Redefining Resource Adequacy Task Force

Today's rapidly increasing levels of wind, solar, storage, and load flexibility require the industry to rethink reliability planning and resource adequacy methods for modern power systems. Periods with a risk of shortfall often no longer coincide with peak demand - reliability risks are less about peak load and more about the daily setting of the sun, extended cloud cover, wind speeds, cold snaps, and heat waves. In addition, demand is increasingly flexible. Key resources are time-sensitive, as batteries need time to recharge and electricity customers can only be asked to provide demand response for just so long. And reliability failures are often correlated - with one another and with the weather. Two driving factors require the industry to reconsider its analytical approach for resource adequacy: (1) Chronological grid operations: The increasing importance of variable renewable resources (such as wind and solar) and of energy-limited resources (storage and demand response) make it essential to understand the full year of chronological operation of the grid. Specific attention must be paid to hourly, seasonal, and inter-annual resource variability. The sequence of the variability is key, as energy-limited resources such as batteries or demand response require either a preceding period or subsequent period of high production to be useful for grid reliability. (2) Correlated events: Historically, resource adequacy analysis focused on shortfalls caused by random, discrete mechanical failures of large generating units. In contrast, shortfalls today are often caused by multiple, correlated events caused by common weather patterns. Resource adequacy analysis must increasingly shift its focus to these correlated events. The redesign of resource adequacy methods will benefit from a set of guiding principles to better allow for sharing of insights and best practices, interregional resource coordination, and a smoother regulatory process for resource procurement. The objective of this report is to move this redesign forward. It provides an overview of key drivers changing the way resource adequacy needs to be evaluated, identifies shortcomings of conventional approaches, and outlines first principles for practitioners to consider as they adapt their approaches. The central message is: what got us here won't get us there.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Envelope-driven comfort risk in residential demand response

Residential demand response (DR) is a valuable resource for grid reliability, but remains challenging because the highly heterogeneous residential building stock leads to widely varying and hard-to-predict load and comfort responses during DR events. Although prior research has estimated the technical potential of DR-capable technologies for achieving energy demand savings, little is known about how they affect thermal comfort. In particular, it remains unclear how indoor thermal conditions due to DR depend on the thermal envelope characteristics of the housing stock. To address this gap, this study provides a systematic, location-specific assessment of indoor thermal performance during DR-events across the US housing stock using both typical DR weather data and detailed building metadata. We evaluate how envelope characteristics influence indoor temperatures during realistic simulated summer and winter DR events across 37 US locations, applying both temperature threshold and rate of temperature change criteria to estimate region-level probabilities of discomfort. Additionally, we show the impact of distinct weather patterns that intensify or abate thermal stress on comfort outcomes. Results show a near-universal overheating risk in summer DR events, where comfort outcomes are strongly influenced by rapid risk of comfort violations. In contrast, overall winter DR discomfort risk is lower, risk escalation is more gradual and shows greater sensitivity to event duration. These findings offer a data-driven quantification of comfort risk across diverse climates and building envelopes, demonstrating the need for region-specific DR scheduling and discomfort mitigation strategies tailored to local weather patterns and the performance of existing residential buildings.

Demand response↗
DOE OSTI2021

Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees

In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.

42 ENGINEERING↗
DOE OSTI2023

Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations

The El Niño Southern Oscillation (ENSO) has profound impacts on weather patterns across the globe, yet there is no consensus on its response to global warming. Several modelling studies find a stronger ENSO in global warming scenarios, while other studies suggest ENSO weakening. Using a broad range of models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and four types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, do predict no change or ENSO weakening, especially in the idealized experiments. Critically, the strongest forcing (abrupt-4xCO 2 ) does not induce the strongest ENSO response, while differences between the models are much greater than those between warming scenarios. For the long-term response (over 1000 years) the models disagree even on the sign of change. Furthermore, changes in ENSO sea surface temperature (SST) variability are only modestly correlated with the tropical Pacific mean state change. The highest correlation for ENSO SST amplitude is found with the mean zonal SST gradient in the SSP5-8.5 experiment (R = –0.58). In contrast, changes in ENSO rainfall variability correlate well with changes in the mean state, as well as with changes in ENSO SST variability. When evaluating the Bjerknes Stability Index for a subset of models, we find that it is not a reliable predictor of ENSO strengthening, as this index tends to predict greater stability with warming. We argue that the enhanced ENSO stability is offset by increases in atmospheric noise or/and potential nonlinear effects. However, a robust inter-model mechanism that can explain a stronger ENSO simulated with global warming is still lacking. Therefore, caution should be exercised when considering ENSO changes based on a single model or warming scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Significant stream chemistry response to temperature variations in a high-elevation mountain watershed

Abstract High-elevation mountain regions, central to global freshwater supply, are experiencing more rapid warming than low-elevation locations. High-elevation streams are therefore potentially critical indicators for earth system and water chemistry response to warming. Here we present concerted hydroclimatic and biogeochemical data from Coal Creek, Colorado in the central Rocky Mountains at elevations of 2700 to 3700 m, where air temperatures have increased by about 2 °C since 1980. We analyzed water chemistry every other day from 2016 to 2019. Water chemistry data indicate distinct responses of different solutes to inter-annual hydroclimatic variations. Specifically, the concentrations of solutes from rock weathering are stable inter-annually. Solutes that are active in soils, including dissolved organic carbon, vary dramatically, with double to triple peak concentrations occurring during snowmelt and in warm years. We advocate for consistent and persistent monitoring of high elevation streams to record early glimpse of earth surface response to warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data from: “Significant stream chemistry response to temperature variations in a high-elevation mountain watershed”

High-elevation mountain regions, central to global freshwater supply, are experiencing more rapid warming than low-elevation locations. High-elevation streams are therefore potentially critical indicators for earth system and water chemistry response to warming. Here we present concerted hydroclimatic and biogeochemical data from Coal Creek, Colorado in the central Rocky Mountains at elevations of 2700 to 3700 m, where air temperatures have increased by about 2 °C since 1980. We analyzed water chemistry every other day from 2016 to 2019. Water chemistry data indicate distinct responses of different solutes to inter-annual hydroclimatic variations. Specifically, the concentrations of solutes from rock weathering are stable inter-annually. Solutes that are active in soils, including dissolved organic carbon, vary dramatically, with double to triple peak concentrations occurring during snowmelt and in warm years. We advocate for consistent and persistent monitoring of high elevation streams to record early glimpse of earth surface response to warming.1) Coal Creek stream were sampled and measured to answer the question if there is a significant stream chemistry response to climate change in high-elevation watershed2) The file contains data regarding discharge, water temperature and stream chemistry data for 2016-2019 year. Stream chemistry includes dissolved organic and inorganic carbon, chloride, nutrients, cations. The data collection efforts were carried out by the DOE-funded "Advancing a Watershed Hydro-Biogeochemical Theory: Linking Water Travel Time and Reaction Rates Under Changing Climate" project and Watershed Function SFA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models

Abstract Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics‐based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop Earth‐system models (ESMs) capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes over long timescales. Building trust in ESMs is a much more difficult problem than for weather forecast models, not least because the model must represent the alternate (e.g., future or paleoclimatic) coupled states of the system for which there are no direct observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML‐based models, demonstrating the credibility of ML‐based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML‐based ESMs to strengthen their credibility and promote their wider use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Gaps and ways forward in atmospheric blocking and extreme weather research

Atmospheric blocking often results in significant weather extremes, such as heatwaves, droughts, cold spells, and floods in mid-latitude regions. However, the physical processes behind blocking and its response to climate change are not well understood, which undermines predictions and decision-making for climate mitigation and adaptation. As a phenomenon with a timescale at the interface of weather and climate, blocking interacts with various elements of the climate system and connects short-term weather events to long-term climate extremes. Understanding atmospheric blocking is crucial, given that climate change may impact its frequency, duration, and geographic distribution. This perspective discusses new experimental approaches to improve our understanding of this important subject.

Wang, Lei [Purdue University, West Lafayette, IN (↗