Amplified threat of tropical cyclones to US offshore wind energy in a changing climate
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17 WIND ENERGY↗
Engineering topics
Xu, Wenwei
Publications and source records attributed to Xu, Wenwei.
Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments
Tropical cyclones (TCs) rank as the deadliest and most financially crippling natural disasters in the United States for the last half-century. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study, we have modeled the intensities of 620 historical TC events in the North Atlantic Basin using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep learning intensity model. By applying a thermodynamic warming signal extrapolated from Global Climate Models, we rerun historical events under eight different future climate scenarios, providing a spectrum of potential TC intensity outcomes. One of the future simulations indicates a staggering 43% increase in the number of major hurricanes, underscoring the critical impact of climate change on TC intensity. Additionally, an interactive dashboard has been created to enable users to explore individual storm simulations and understand the influence of future climate signals on environmental conditions of TC development and resulting TC intensities. This dataset and the user-friendly tool offer invaluable resources for systematic exploration of the discrete effects that changes in the air-sea thermodynamic state have on the intensities of TCs.
Climate Change↗
A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model
Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.
54 ENVIRONMENTAL SCIENCES↗
A Global Increase in Nearshore Tropical Cyclone Intensification
Abstract Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.
54 ENVIRONMENTAL SCIENCES↗
Representing farmer irrigated crop area adaptation in a large-scale hydrological model
Abstract. Large-scale hydrological models (LHMs) are commonly used for regional and global assessment of future water shortage outcomes under climate and socioeconomic scenarios. The irrigation of croplands, which accounts for the lion's share of human water consumption, is critical in understanding these water shortage trajectories. Despite irrigation's defining role, LHM frameworks typically impose trajectories of land use that underlie irrigation demand, neglecting potential dynamic feedbacks in the form of human instigation of and subsequent adaptation to water shortages via irrigated crop area changes. We extend an LHM, MOSART-WM, with adaptive farmer agents, applying the model to the continental United States to explore water shortage outcomes that emerge from the interplay between hydrologic-driven surface water availability, reservoir management, and farmer irrigated crop area adaptation. The extended modeling framework is used to conduct a hypothetical computational experiment comparing differences between a model run with and without the incorporation of adaptive farmer agents. These comparative simulations reveal that accounting for farmer adaptation via irrigated crop area changes substantially alters modeled water shortage outcomes, with US-wide annual water shortages being reduced by as much as 42 % when comparing adaptive and non-adaptive versions of the model forced with US climatology from the period 1950–2009.
54 ENVIRONMENTAL SCIENCES↗
A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset
Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 40,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with the observed spatial distribution of TC tracks and basin-scale TC statistics. Specifically, along the coast, spatial variations in TC crossing probability and extreme winds upon landfall are well-reproduced by RAFT with R-squared values of 0.81 and 0.73, respectively. In summary, the synthetic TC database constructed with RAFT provides a reasonable pathway for the robust assessment of North Atlantic TC wind and rainfall risks.
54 ENVIRONMENTAL SCIENCES↗
A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters
Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.
54 ENVIRONMENTAL SCIENCES↗
A North Atlantic synthetic tropical cyclone tracks, intensity, and rainfall dataset
Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales where their impacts are most prominent. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 50,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with observations for spatial distribution of TC tracks and basin-scale distributions of TC translation speeds, lifetime maximum intensities and intensification rates. Also, spatial variations in coastal frequency and precipitation for landfalling TCs are well-reproduced in RAFT. In summary, the synthetic TC database based on RAFT provides a reasonable pathway for robust assessment of TC wind and rainfall risk for the US coastal regions and other areas affected by Atlantic TCs.
Xu, Wenwei↗
Increased U.S. coastal hurricane risk under climate change
Several pathways for how climate change may influence the U.S. coastal hurricane risk have been proposed, but the physical mechanisms and possible connections between various pathways remain unclear. Here, future projections of hurricane activity (1980–2100), downscaled from multiple climate models using a synthetic hurricane model, show an enhanced hurricane frequency for the Gulf and lower East coast regions. The increase in coastal hurricane frequency is driven primarily by changes in steering flow, which can be attributed to the development of an upper-level cyclonic circulation over the western Atlantic. The latter is part of the baroclinic stationary Rossby waves forced mainly by increased diabatic heating in the eastern tropical Pacific, a robust signal across the multimodel ensemble. Last, these heating changes also play a key role in decreasing wind shear near the U.S. coast, further aggravating coastal hurricane risk enhanced by the physically connected steering flow changes.
54 ENVIRONMENTAL SCIENCES↗
Exploring the Consistency of Water Scarcity Inferences between Large-Scale Hydrologic and Node-Based Water System Model Representations of the Upper Colorado River Basin
Water resources model development and simulation efforts have seen rapid growth in recent decades to aid evaluations and planning around water scarcity and allocation. Models are typically developed by two distinct communities: (1) large-scale hydrologic modelers emphasizing hydroclimatological processes, and (2) water systems modelers emphasizing environmental, infrastructural, and institutional features that shape water scarcity at the local basin level. This study assesses whether two representative models from these communities produce consistent insights when evaluating the water scarcity vulnerabilities in the Upper Colorado River Basin within the state of Colorado. Results showed that although the regional-scale model [model for scale adaptive river transport (MOSART)—water management (WM)] can capture the aggregate effect of all water operations in the basin, it underestimates the subbasin-scale variability in specific user’s vulnerabilities. The basin-scale water systems model [State of Colorado’s Stream Simulation Model (StateMod)] suggests a larger variance of scarcity across the basin’s water users due to its more detailed accounting of local water allocation infrastructure and institutional processes. This model intercomparison highlights potentially significant limitations of large-scale studies in seeking to evaluate water scarcity and actionable adaptation strategies, as well as ways in which basin-scale water systems model’s information can be used to better inform water allocation and shortage when used in tandem with larger-scale hydrological modeling studies.
54 ENVIRONMENTAL SCIENCES↗
Increasing Hurricane Intensification Rate Near the US Atlantic Coast
Hurricanes often cause severe damage and loss of life, and storms that intensify close to the coast pose a particularly serious threat. While changes in hurricane intensification and environment have been examined at basin scales previously, near-coastal changes have not been adequately explored. In this study, we address this using a suite of observations and climate model simulations. Over the 40-year period of 1979–2018, the mean 24-hr hurricane intensification rate increased by ~1.2 kt 6-hr –1 near the US Atlantic coast. However, a significant increase in intensification did not occur near the Gulf coast over the same period. The enhanced hurricane intensification along the Atlantic coast is consistent with an increasingly favorable dynamic and thermodynamic environment there, which is well simulated by climate models over the historical period. Further, multi-model projections suggest a continued enhancement of the storm environment and hurricane intensification near the Atlantic coast in the future.
54 ENVIRONMENTAL SCIENCES↗
The Third Assessment of the Effects of Climate Change on Federal Hydropower
Understanding the future changes in projected water supplies is a vital objective for federal hydropower facilities tasked with providing low-cost, reliable electricity across a large regional footprint that encompasses a growing customer base, alternative market structures for marketing the electricity, and a more diverse generation asset mix than was historically present when a majority of federal hydropower facilities were built. This study, The Third Assessment of the Effects of Climate Change on Federal Hydropower, directed by Section 9505 of the SECURE Water Act of 2009 (SWA), is the third quinquennial report on evaluating the effects of climate change on hydroelectric energy generated from 132 US federal hydropower plants marketed by four US Department of Energy (DOE) Power Marketing Administrations (PMAs). The technical assessment is conducted by DOE’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Texas A&M University under the guidance of DOE’s Water Power Technologies Office. This study is the result of extensive consultation with the four federal PMAs (Bonneville Power Administration [BPA], Western Area Power Administration [WAPA], Southwestern Power Administration [SWPA], and Southeastern Power Administration [SEPA]), as well as other agencies, including federal hydropower owners/operators (the US Army Corps of Engineers, US Bureau of Reclamation [Reclamation]), US Geological Survey, and National Oceanic and Atmospheric Administration). The main findings of this assessment, along with the PMA administrators’ recommendations, will be included in a subsequent DOE report to Congress. The assessment method and the technical findings are described in this report.
13 HYDRO ENERGY↗
Deep Learning Experiments for Tropical Cyclone Intensity Forecasts
Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.
58 GEOSCIENCES↗
Water Resource Opportunities at Lake Gazivode/Ujmani
The U.S. Department of Energy’s (DOE’s) Pacific Northwest National Laboratory (PNNL) has been tasked by DOE’s Office of International Affairs to assess the use of water resources for power generation needs on Lake Gazivode/Ujmani in Kosovo, and provide recommendations for improved coordination and efficiency. Lake Gazivode/Ujmani is a 15-mile long man-made reservoir that straddles the Serbian-Kosovo border. Lake Gazivode/Ujmani is currently managed without a transboundary cooperation agreement. Kosovo is profoundly dependent on the lake’s waters, which provide one-third of Kosovo’s drinking water and cool two coal plants that provide 95 percent of Kosovo’s energy production. After conducting a scoping visit to Pristina, Kosovo, Lake Gazivode/Ujmani, and Belgrade, Serbia, in October 2020, PNNL staff compiled hydrometeorological, water management operations, and power grid operations data. They analyzed the data and existing literature to provide third-party observations about and recommendations for the use of the lake. Their recommendations aim to promote regional water and energy security.
13 HYDRO ENERGY↗
Pronounced Impact of Salinity on Rapidly Intensifying Tropical Cyclones
We show the importance of salinity for rapidly intensifying Atlantic tropical cyclones and demonstrate the potential for improved prediction of rapid intensification through the inclusion of salinity. Tropical Cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well-known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon-Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical-dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near real-time.
Balaguru, Karthik↗