Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization
Not Available
Wu, Wei-Cheng (ORCID:0000000222156351)↗
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Results for “extreme event”
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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Towards robust and scalable dispatch modeling of long-duration energy storage
Not Available
Guerra, Omar J.↗
Antecedent conditions determine the biogeochemical response of coastal soils to seawater exposure
Coastal landscapes are increasingly exposed to seawater due to sea level rise and extreme weather events. The biogeochemical responses of these vulnerable ecosystems are poorly understood, limiting our ability to predict how their role in local and global biogeochemical cycles will shift under future conditions. Here we evaluate how antecedent conditions influence the biogeochemical response of soil to seawater inundation events based on a 42-day laboratory incubation experiment with soils collected from a natural salinity gradient across a coastal floodplain. We quantified influences of seawater inundation on intact soil cores through high-frequency carbon dioxide (CO 2 ) and methane (CH 4 ) gas fluxes measurements as well as ultrahigh resolution characterization of organic matter chemistry and metabolites. Mean CO 2 and CH 4 fluxes were higher after inundation compared to control cores for soils that had low in situ salinity. Soils with low in situ salinity also exhibited significant shifts in organic matter profiles after inundation, with surficial soils (0-7.5 cm) becoming more enriched in phenolic compounds, compared to deeper soils (7.5-15 cm). The number of biochemical transformations inferred from mass spectrometry increased significantly after inundation for soils with low in situ salinity, and a trend of decreasing thermodynamic favorability with increasing salinity was observed, suggesting an increase in microbial activity following inundation. Our results suggest that seawater inundation of low-salinity terrestrial environments can lead to increased microbial activity, preferential depletion of more thermodynamically favorable compounds, and increased soil carbon release. Finally, we conclude that the biogeochemical impacts of future seawater exposure will be modulated by antecedent conditions associated with landscape position within coastal watersheds.
54 ENVIRONMENTAL SCIENCES↗
Automated defect identification in electroluminescence images of solar modules
Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.
14 SOLAR ENERGY↗
Floodplain Inundation and Salinization From a Recently Restored First‐Order Tidal Stream
Not Available
54 ENVIRONMENTAL SCIENCES↗
Dynamic Amplification of Subtropical Extreme Precipitation in a Warming Climate
Projected precipitation changes in a warming climate vary considerably, spatially, and between intensities. The changes can be greater or less than the ~7% K -1 Clausius-Clapeyron (CC) prediction, owing to dynamic effects. Using two global-climate-model large ensembles, we quantify the dynamically induced changes to precipitation extremes from the present (1996–2005) to late-21st-century (2071–2080) climates, as a function of recurrence interval, focusing on the subtropics. We separate non-CC changes into a term proportional to the present-day vertical-velocity spatial pattern (i.e., an amplification or damping thereof by a constant factor) and a residual. The amplitude term varies with recurrence interval, approximately canceling (doubling) CC for moderate (large) extremes, increasing precipitation variability. Contrastingly, the residual is quasi-uniform across recurrence intervals but spatially heterogeneous, weakening extremes over dry zones. This residual may be related to Hadley cell expansion, although this explanation is insufficient to explain many features, and other possible mechanisms are discussed.
54 ENVIRONMENTAL SCIENCES↗
Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning
Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.
54 ENVIRONMENTAL SCIENCES↗
The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information
Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.
54 ENVIRONMENTAL SCIENCES↗
Uncertainty in Simulating Twentieth Century West African Precipitation Trends: The Role of Anthropogenic Aerosol Emissions
Anthropogenic aerosol emissions from North America and Europe have strong effects on the decadal variability of the West African monsoon (WAM). Anthropogenic aerosol effective radiative forcing is model dependent, but the impact of such uncertainty on the simulation of long-term WAM variability is unknown. We use an ensemble of simulations with HadGEM3-GC3.1 that span the most recent estimates in simulated anthropogenic aerosol effective radiative forcing. We show that uncertainty in anthropogenic aerosol radiative forcing leads to significant uncertainty at simulating multi-decadal trends in West African precipitation. At the large scale, larger forcing leads to a larger decrease in the interhemispheric temperature gradients, in temperature over both the North Atlantic Ocean and northern Sahara. There are also differences in dynamic changes specific to the WAM (locations of the Saharan heat low and African Easterly Jet, of the strength of the West African westerly jet, and of African Easterly Wave activity). We also assess effects on monsoon precipitation characteristics and temperature. We show that larger aerosol forcing results in a decrease of the number of rainy days and of heavy and extreme precipitation events and warm spells. However, simulated changes in onset and demise dates do not appear to be sensitive to the magnitude of aerosol forcing. Our results demonstrate the importance of reducing the uncertainty in anthropogenic aerosol forcing for understanding and predicting multi-decadal variability in the WAM.
54 ENVIRONMENTAL SCIENCES↗
Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks
Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
54 ENVIRONMENTAL SCIENCES↗
Differential Trends in Iron Concentrations of Boreal Streams Linked to Catchment Characteristics
Increasing iron (Fe) concentrations have been reported for freshwaters across northern Europe over the last decades. This increase, together with elevated concentrations of dissolved organic carbon (DOC), leads to browning of freshwaters, which affects aquatic organisms, ecosystem functioning, biogeochemical cycles, and brings challenges to drinking water production. However, how such increasing trends in stream Fe concentrations reflect the contribution of different catchment sources remains poorly resolved. Here, we explored how catchment characteristics, that is, mires and coniferous soils, regulate spatial and temporal patterns of Fe in a boreal stream network. For this, we determined Fe speciation in riparian and mire soils, and studied temporal Fe dynamics in soil-water and stream-water over a span of 18 years. Positive Fe trends were found in the solution of the riparian soil, while no long-term trend was observed in the mire. These differences were reflected in stream-water, where three headwater streams dominated by coniferous cover also displayed positive Fe trends, whereas the mire dominated stream showed no trend. Surprisingly, the majority of higher order streams showed declining Fe trends, despite long-term increases in DOC. In addition, we found that an extreme drought event led to a prolonged release of Fe and DOC from the riparian soils, that could have long-term effects on stream Fe concentrations. Our results show that riparian forest soils can be major contributors to ongoing increases in freshwater Fe concentrations and that drought can further promote the release of Fe from organic soils.
54 ENVIRONMENTAL SCIENCES↗
Evaluating Large‐Storm Dominance in High‐Resolution GCMs and Observations Across the Western Contiguous United States
Abstract Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.
Environmental Sciences & Ecology↗
Analytical Model for the Higher Order Moments of Midlatitude Atmospheric Temperature Distributions
Observed distributions of atmospheric temperature are non-Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid-latitude temperature variability based on symmetric advection from a non-symmetric background temperature profile. We then use this model to derive analytical expressions for the higher order moments of temperature distributions. Our results show that nonzero skewness and kurtosis arise due to the nonlinearity of the time-mean meridional temperature profile. The analytical model matches an idealized Held-Suarez atmospheric model, indicating nonlinearity of time-mean temperature in latitude is the dominant contribution to nonzero skewness and kurtosis in synoptic temperature variations. Model analysis further shows decrease in higher order moments due to climate change come roughly equally from changes in mixing length and changes in the background temperature profiles.
54 ENVIRONMENTAL SCIENCES↗
How Representative Are Uncrewed Aircraft System Measurements of the Convective Boundary Layer?
Abstract Uncrewed aircraft systems (UAS) demonstrate significant potential for filling data gaps in the atmospheric boundary layer. However, the extent to which UAS observations—typically vertical profiles taken over 15 min—are representative of the boundary layer as a whole remains poorly characterized. Using large eddy simulations (LES) of the daytime convective boundary layer (CBL), we quantify random errors in UAS measurements that occur due to insufficient statistical convergence of the time average to the true ensemble mean. Random errors in first‐order moments increase as the CBL becomes increasingly unstable, and are largest near the surface for most quantities. Errors are on the order of 2–6 m for wind speed, 15–60 for wind direction, 0.2–3 K for potential temperature, and 0.1–1 g for specific humidity, with errors in turbulent fluxes on the order of 50%–100%. Sampling strategies that mitigate random errors are discussed in light of our results.
Greene, Brian R. [Now at Verisk Extreme Event Solu↗
Impact of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on the US Puget Sound Regional Hydroclimate
El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) are two major modes of climate variability with global hydroclimate impacts. However, their impacts often depend on the local climate and geography, resulting in large regional differences. In this study, we examined the connection of ENSO and MJO to the hydroclimate conditions and extremes in the Puget Sound (PS) basin located in the US Pacific Northwest coast. The results indicate that ENSO significantly modulates the cold season temperature and temperature-mediated hydrologic processes. El Niño cold seasons feature less snow accumulation and intensified surface runoff, even if the precipitation amount is similar to La Niña cold seasons. Therefore, El Niño causes more snow drought (in the form of compound dry and warm snow drought) and shifts the surface runoff seasonality by reducing runoff in the subsequent warm season. MJO phases 6–7 trigger more extreme precipitation, temperature, snowmelt, and runoff in the PS region at 0–9–day lags, and such connections are robust regardless of how the ENSO signals are removed. Meanwhile, MJO modulates large-scale extreme weather systems (e.g., atmospheric rivers) with significant enhancement during phases 6–7. ENSO impacts have intensified in the 2001–2020 period, whereas MJO impacts showed some phase shift in this period. This study reveals ENSO and MJO phases 6–7 as useful predictors of the PS hydroclimate anomalies/extremes at seasonal and daily scales, respectively. Utilizing these findings holds the potential to improve regional water resources prediction and management.
ENSO↗
Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates
The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.
equity↗
Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach
Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.
Abadi, Azar M. [University of Alabama, Birmingham,↗
Multidecadal Fluctuations in the Observed ENSO‐Tropical Cyclone Teleconnection
Abstract El Niño‐Southern Oscillation (ENSO) is a skillful predictor for seasonal tropical cyclone (TC) activity in most TC basins. This study examines recent changes in the observed ENSO‐TC teleconnection strength, as measured by ENSO modulation of hurricane frequency. We find that the ENSO‐North Atlantic TC teleconnection fluctuated over time, with the strongest relationship occurring from the 1980s to the mid‐2000s. In the western and eastern North Pacific, the ENSO‐TC teleconnection has strengthened in recent decades. Periods with a strong ENSO‐TC teleconnection are associated with more favorable environmental conditions for TCs, with higher values of genesis potential indices. Positive phases of the Atlantic Multidecadal Oscillation coincided with periods of strong ENSO‐TC teleconnections in the Atlantic and North Pacific basins. A weaker Atlantic ENSO‐TC relationship was associated with negative phases of the Pacific Decadal Oscillation and the North Atlantic Oscillation. This research reveals climate conditions that modulate ENSO's utility for seasonal TC prediction. Plain Language Summary El Niño‐Southern Oscillation (ENSO) is a useful predictor for seasonal tropical cyclone (TC) activity in many basins. Here we found that the strength of the ENSO‐TC teleconnection, represented as the correlation between ENSO and the number of hurricanes and accumulated cyclone energy, has changed in the historical record. The ENSO‐TC teleconnection in the North Atlantic fluctuated over time, with a weak relationship during the 1960s and 1970s and a strong relationship during the 1980s to mid‐2000s. Meanwhile, the ENSO‐TC teleconnection strengthened in the North Pacific in recent decades, with strong teleconnections after the 1980s in the western North Pacific and after the 2000s in the eastern North Pacific. Periods of strong ENSO‐TC teleconnections are associated with more favorable environmental conditions for TCs, including higher values of genesis potential indices and higher mid‐tropospheric humidity, as well as positive phases of the Atlantic Multidecadal Oscillation. Additionally, the negative phase of the Pacific Decadal Oscillation leads to strong/weak ENSO‐TC teleconnections in the eastern North Pacific and North Atlantic, respectively. Furthermore, a negative North Atlantic Oscillation is associated with a weak ENSO‐North Atlantic TC teleconnection. This research highlights variations in ENSO's effectiveness for seasonal TC prediction. Key Points The observed impact of ENSO on tropical cyclone (TC) activity exhibits multidecadal fluctuations The ENSO‐TC teleconnection was strong in the Atlantic from the 1980s to mid‐2000s and strengthened over the North Pacific in recent decades The ENSO‐TC teleconnection is stronger in the Atlantic and North Pacific basins during a positive Atlantic Multidecadal Oscillation
ENSO↗