Intense Storms Research Articles

Under-reported and under-served: Disparities in US disaster federal aid-to-damage ratios after hurricanes

The level of outlays that individuals and communities receive following disasters influences the rapidity by and the degree to which they recover. While there is no prescribed formula for the level of aggregate federal aid a county receives, one might expect it to be proportional to the damage sustained. In actuality, the fraction of damages that are covered by disaster aid (which we call “federal disaster coverage”) is highly variable. In this work, we investigate the county-level correlates of federal disaster coverage using hurricanes that received Presidential Disaster Declarations from 2008 to 2017 by asking (1) What county and hazard characteristics are important predictors of counties that receive aid but that do not incur damage? and (2) Where damage is reported, what county and hazard characteristics influence federal disaster coverage? We find that counties that receive aid but have no reported damage are more likely to experience greater storm intensity and have more hazard exposure than observations that do not receive aid, suggesting that these counties’ damages are unreported. Concerningly, these counties also exhibit greater social vulnerability. Among counties that report damage, we find that federal disaster coverage decreases as hazard intensity and per capita damage increase, suggesting that more severe disasters receive less marginal aid than less severe disasters. Higher local capacity increases the likelihood of aid and the level of coverage. Overall, our findings suggest disparities in how disaster damages are reported in major comprehensive disaster datasets and in how federal aid is disbursed among counties.

Rain-Based Train Washing: A Sustainable Approach to Reduce PM Concentrations in Underground Environments

Fine particle concentrations measured in many underground rail systems around the world consistently exceed those observed at ground level, potentially posing significant implications for human health. While numerous authors have observed these high particle concentrations and analyzed both their atomic compositions and health impacts, few have investigated devices and technologies capable of reducing these high levels in underground environments. In light of these considerations and recognizing the multifaceted challenges associated with maintaining air quality in underground metro systems, the aim of this paper was to evaluate the usefulness and effectiveness of utilizing rainwater for washing trains to abate particulate matter (PM) concentrations in underground rail systems. To achieve this aim, an ad hoc case study was considered: the Naples Metro Line 1 (Italy), which is characterized by 4.5 km in the ground level and 13.5 km underground. A measurement campaign was carried out during storms of strong intensity through PM measuring instruments placed on station platforms along the metro line. Precisely, the trains were washed by the rain in the initial ground level section, and then continued wet within the underground one. The results of this measurement campaign were compared with those of a comparable survey carried out during average clear weather conditions, and the results showed that the train washing produces a significant PM10 concentration reduction of up to about 60% in the underground environment. If confirmed in other experimental settings, these results could lay the groundwork for the introduction of structured washing system devices (e.g., periodically washing trains and/or tunnels) for the reduction of PM concentration in underground metro systems. The present study sought to contribute valuable insights towards sustainable and environmentally conscious approaches to addressing air quality concerns, particularly by harnessing the natural resource of rainwater during specific meteorological events.

Assessing coastal vulnerability and climate resilience for Caribbean small island states using ecological principles

Climate Change challenges small island developing nations (SIDS) whose economies are heavily dependent on touristic coastal development. Climate change, as a driver of coastal impacts, is manifested by increased storm intensity as well as sea level rise. The intersection of climate hazards, exposure and vulnerability is the overall risk of coastal impacts. Ecological principles can be applied to simple Coastal Indices (CVI) to establish priorities for mitigation focused on risky development areas to direct population growth to lower risk areas. This research examines one island within the Bahamian archipelago to demonstrate how ecological principles can be applied to quantify vulnerability and recommend high-priority sites for restoration or remediation. This study aims to quantify vulnerability of coastlines through an assessment of coastal vegetation setbacks, intact wetlands and coastal geomorphology. The aim is to produce a tool to identify highly vulnerable/highly exposed coastlines that can be specifically targeted for risk reduction through cost-benefit analyses of response options. The vulnerability assessment was quantitative and allowed the ranking of coastal environments on one island to set priorities for remediation of highly vulnerable sites. Tools that identify at-risk coastal areas allow small Caribbean nations more time to adapt their infrastructure and relocate populations.

Establishing an Early Warning System for Dust Storms in Peri-Desert Regions

The Taklimakan Desert in northwest China stands as a significant contributor to dust storms, with its fringe oases already designated as ecologically fragile due to the severe impacts of these storms. This study focuses on Moyu County, situated on the southwest edge of the Taklimakan Desert, examining the origin and transport pathways of dust storms from 2004 to 2021. The classification involves utilizing a 36 h backward trajectory model and the k-means clustering technique, resulting in three clusters displaying distinct transport pathways and entry directions. Air pollutant concentrations at the study site corresponding to each cluster are analyzed to elucidate the contribution of dust storms from different directions. The results categorize 1952 dusty days into three categories: NE-SE (cluster 1), N-N (cluster 2), and NW-W (cluster 3). The highest frequency of dust storms, accounting for 64% of the total suspended dust weather, originates from the northeast and southeast direction (NE-SE category), with relatively weak intensity, mainly as suspended dust (71.5%). Strong sand storms predominantly occur from the northwest direction (57.8%). Cluster 1 (the southeast direction) exhibits a higher concentration of SO2, NO2, and CO, mainly associated with its pathway over anthropogenically polluted areas. Conversely, Cluster 3 (northwest direction) shows higher PM10 and PM2.5 concentrations due to increased wind speed and stronger dust storm intensity. The study develops dust storm early warning schemes based on 15-day advance predictions, utilizing an 18-year trajectory model and local monitoring data. This proposed warning scheme serves as a predictive tool for potential dust storm events and air pollution levels, aiding in both scientific research and policy formulation for dust storm mitigation and adaptation. The data obtained also hols relevance for conducting further scientific research in this field.

Assessment of model projections of climate‐change induced extreme storms on the south‐east coast of Australia

AbstractGeneral circulation models (GCMs) and their downscaled regional‐scale equivalents have been important tools for climate‐change studies. However, there has been limited assessment of the performance of GCMs and downscaled models in simulating extreme storms in temperate coastal environments. This study assesses the model characterization of extreme storms on the heavily populated coast of south‐east Australia. Twenty‐year average recurrence interval (ARI) storm intensities derived from generalized extreme value (GEV) distributions based on observed and large‐scale atmospheric model data are compared. Changes in extreme storms from past climate to a high‐emission future scenario are also investigated. Simulations of storm minimum surface pressures compared favourably with measured data. Both the GCMs and downscaled models reproduced the observed decrease with increasing latitude along the coast in the 20 year ARI of minimum surface pressure. Both indicated that the minimum storm surface pressure should change negligibly in a high‐emission future. Although the models underestimated the maximum daily precipitation significantly, models are improving significantly with CMIP epoch and downscaling. In the high‐emission future scenario, the GCMs and NARCliM projected the 20 year ARI maximum daily precipitation would increase in the order of 25%. GCMs and the corresponding downscaled products presently do not represent the extreme value distributions of historical wind speed data well, overestimating at smaller values of ARI and significantly underestimating in larger values of ARI. Significant changes in the magnitude of the 20 year ARI maximum daily‐average onshore wind speed are not projected for the high‐emission future.

Interdecadal variability of the pre-monsoon cyclone characteristics over the Bay of Bengal

The low-pressure systems intensified to cyclones prior to the onset of the Indian summer monsoon season over the North Indian Ocean are referred to as pre-monsoon season (PMS) cyclones. Climate change is amplifying the pre-monsoon cyclone landscape, fostering more frequent and intense storms with altered tracks, resulting in heightened risks for coastal communities and economies. This study investigates the interdecadal variations in tropical cyclone and key large-scale atmospheric parameters that influence the characteristics of cyclones, including track, frequency over the Bay of Bengal (BoB) during the pre-monsoon season from 60 years of data. The large-scale atmospheric parameters are analyzed by calculating climatological anomalies. It is noted that the frequency of cyclones making landfall over the eastern Indian coastal landmass has increased in the recent decade compared to the past five decades. Compared to the past fifty years, the percentage frequency has increased in the recent ten years by 50%. At the low level, stronger easterlies are dominant and upper-level jet streams shift to lower latitudes, indicating that the path of cyclones has shifted from the north (N)-northeast (NE) to the northwest (NW), i.e. towards the east Indian coastal landmass, over the recent decade. In contrast to the previous five decades, an unusual low-pressure region has emerged over the NW India and Pakistan regions, creating a favorable path for cyclones moving towards the Indian region in recent decade. Cyclones have been more intense in the recent decade than they were in the previous five decades, according to the rise in low- and mid-level specific humidity and temperature over the BoB.

Combined use of HYSPLIT model and MODIS aerosols optical depth to study the spatiotemporal circulation patterns of Saharan dust events over Central Europe

Mineral dust released from the desert region and transported into the atmosphere has a crucial impact on the Earth's climate system's biogeochemical cycle. It has serious adverse effects on human health. The Sahara is one of the world's dustiest areas. This investigation intends to uncover the underlying reasons for atmospheric dust dispersion throughout the year by tracking the dusttransport and deposition in Central Europe, focusing on arid areas of North Africa. In this paper, we use the GDAS (Global Data Assimilation System) archival meteorological database to compute the analytical forward trajectories and configure the particle concentrations using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model. Besides, we investigate the synoptic meteorological conditions of acute Saharan dust episodes to determine the dynamic atmospheric system during their onset. The forward trajectories reveal the seasonality of wind-blown dust throughout the year. Dust storms are typically more prevalent in the spring, with a second peak in the Summer. As a result, particle transport takes various paths as the seasons and climatic conditions change. The most dust-laden masses, which reach high altitudes from the source areas, are often transported to Central Europe, where their seasonal distribution is relatively similar to that of the studied African region. However, the intensity and frequency of Saharan dust events (SDEs) have significantly changed in the previous decades, with an increased number of intense winter storms. According to the synoptic analysis, this variability is strongly linked to two factors. (1) The intensity and lifetime variation of the Mediterranean cyclones and (2) Climate change triggered lee-side (Sharav) cyclogenesis modified by the topographic complexity of Atlas. This study also confirmed the effectiveness of the HYSPLIT model in simulating atmospheric dust after comparing it with annual aerosol optical depth measurements from MODIS (Moderate-Resolution Imaging Spectroradiometer) data.

Ground Electric Field, Atmospheric Weather and Electric Grid Variations in Northeast Greece Influenced by the March 2012 Solar Activity and the Moderate to Intense Geomagnetic Storms

In a recent paper, we extended a previous study on the solar solar influence to the generation of the March 2012 heatwave in the northeastern USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), Interplanetary space (ACE satellite), the Earth’s magnetosphere (Earth-based measurements, NOAA-19 satellite), the top of the clouds (Terra and Aqua satellites), and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, the same day as the heatwave started in USA, (ii) during the March 2012 winter-like event in Thrace (6–15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (>500 MeV), (iii) Between 3–31 March, the temperature in Alexandoupoli and the ACE/EPAM solar high energy (1.88–4.70 MeV) proton flux were strongly anticorelated (r = −0.75, p = 0.5). (iv) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8–10 and 12–13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (v) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~−2000 V/m and ~1800 V/m on 8 March, (vi) the winter-like weather on 8–10 March 2012 occurred after the detection of the main SEP event related with a coronal mass ejection released in interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8–10 March weather was related with a deep drop of ~63 °C in the cloud top temperature measured by MODIS/Terra, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~41°N) and we found, for the first time sudden voltage changes of ~3.5 kV in the electric grid in Greece, during the decay phase of the March 2012 storm series. We discuss the winter-like March 2012 event in Thrace regarding the influence of solar cosmic rays on the low troposphere mediated by positive North Atlantic Oscillation (NAO). Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

An Assessment of Solar Cycle 25 progress through observation of SRBs and associated Geomagnetic Storms

Geomagnetic storms are severe aspects of Space Weather. They originate due to solar transient emissions such as coronal mass ejections (CMEs), whose energetic materials propagate in the Interplanetary medium and are coupled with the magnetosphere system. CME driven Geomagnetic storms are often associated with solar radio bursts (SRBs), particularly type II and type IV bursts. In this study, we present the preliminary results of solar radio observations and their associated geomagnetic activity during solar cycle 25 (SC 25) from January 2020 to June 2023, focusing on the cycle’s first four intense geomagnetic storms. The study used various radio telescopes, mainly the compound astronomical low-frequency low-cost instrument for spectroscopy and transportable observatory (CALLISTO), as well as OMNI data and the World Data Center for Geomagnetism. During the study period, it was found that 23 solar radio bursts diagnosed the geomagnetic storms with Dst <-50nT from 35 reported, including three severe storms of the SC 25. The time delay between the solar radio bursts and the arrival of CMEs and/or HSS near the Earth’s magnetosphere is estimated with an average value of 79 h within the [48–120 h] range for 23 geomagnetic storms associated with solar radio bursts. Among 35 geomagnetic storms recorded, five are recurring geomagnetic storms associated with coronal high-speed streams (HSS), while CMEs cause the rest with average speeds of 750 km/s. The current SC 25 recognizes four major storms within the scope of the study. On 21 April 2023, a type II radio burst followed by a type IV burst diagnosed the first severe geomagnetic storm on 24 April 2023. The second severe storm was unusual and detected in the absence of the precursor as a solar radio burst. The SRBs of type II burst and type IV burst extending in IP medium on 1 November 2021 tracked the third major storm of the cycle while the group of type III radio bursts, type II and type IV bursts on 24 February 2023, predicted the major storm on 27 February 2023. These major geomagnetic storms are linked to CMEs that show expanding flux ropes, which are signatures of type II and moving type IV radio bursts identified. Furthermore, the detected SRBs and related major geomagnetic storms are proof of high solar and magnetic activity of the ascending phase of SC 25. The SC 25 has been characterized overall, and its current progress is being tracked using observations of SRBs and magnetic activity during its rising phase.

Impacts of Harmful Algal Blooms (HABs) on Agriculture: A Short Communication

agriculture, which requires clean water, healthy soil, and adequate nutrients, is crucial to meet the growing demand for food and fiber. Over application of nutrients to meet demand has degraded surface water quality, leading to accelerated eutrophication. Cultural eutrophication is a process by which aquatic ecosystems such as ponds, lakes, and estuaries become so enriched with nutrients—primarily nitrogen and phosphorus—as to become unusable for safe consumption and ecological purposes. Eutrophication has intensified due to climate change. Increased temperatures, intense storms, and drought can drive the formation of eutrophic and hypereutrophic conditions. Hypereutrophication results in the rapid proliferation of algae and phytoplankton, resulting in algal blooms. Harmful algal blooms (HABs), including the proliferation of cyanobacteria, which can produce cyanotoxins such as microcystins and cylindrospermopsin, can also grow in hypereutrophic conditions. These toxins can detrimentally impact humans, wildlife, and agricultural systems (e.g., fish, livestock, and crops). Fish that ingest algal toxins may suffer from liver damage and oxidative stress, with varying effects depending on the type of fish, the amount of exposure, and the duration of exposure to toxins. Additionally, a few studies report livestock death after drinking water contaminated with byproducts of HABs such as microcystin, nodularin, cylindrospermopsin, anatoxin-a, guanitoxin, and saxitoxin. The available research on the effects of microcystin on crops, vegetables, and fruits consistently demonstrates their negative impact. This short communication article summarizes the literature published between 2012 and 2022 documenting the impact of microcystins on agricultural commodities, particularly livestock and various crops. Although awareness has increased, few publications have discussed microcystin-related livestock deaths in recent years. However, numerous global studies have highlighted the harmful effects of microcystins on crops, fruits, and vegetables. The researchers suggest that in cases where the levels of microcystin exceed the standards established by the World Health Organization, careful monitoring is needed. Human exposure to microcystin may occur through the consumption of livestock, crops, and vegetables contaminated by microcystins through drinking or irrigation. More research is needed to understand the fate and transport mechanisms of microcystins in various agricultural settings, including controlled, simulated, and field experiments.

Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

Abstract The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting. Significance Statement Global numerical weather prediction systems provide important guidance to forecasters about tropical cyclone development, motion, and intensity. Despite recent improvements in the Canadian operational model’s ability to predict tropical cyclone formation, the system systematically underpredicts the intensity of these storms. In this study, we use a set of increasingly simplified experiments to identify the source of this error, which lies in the numerical time-stepping scheme used to solve the model equations. By decreasing numerical drag on the tropical cyclone circulation, intensity predictions that resemble those of other global modeling systems are achieved. This will improve the quality of Canadian tropical cyclone guidance for forecasters around the world.

Significant Midlatitude Bubble‐Like Ionospheric Super‐Depletion Structure (BLISS) and Dynamic Variation of Storm‐Enhanced Density Plume During the 23 April 2023 Geomagnetic Storm

AbstractThis paper investigates the midlatitude ionospheric disturbances over the American/Atlantic longitude sector during an intense geomagnetic storm on 23 April 2023. The study utilized a combination of ground‐based observations (Global Navigation Satellite System total electron content and ionosonde) along with measurements from multiple satellite missions (GOLD, Swarm, Defense Meteorological Satellite Program, and TIMED/GUVI) to analyze storm‐time electrodynamics and neutral dynamics. We found that the storm main phase was characterized by distinct midlatitude ionospheric density gradient structures as follows: (a) In the European‐Atlantic longitude sector, a significant midlatitude bubble‐like ionospheric super‐depletion structure (BLISS) was observed after sunset. This BLISS appeared as a low‐density channel extending poleward/westward and reached ∼40° geomagnetic latitude, corresponding to an APEX height of ∼5,000 km. (b) Coincident with the BLISS, a dynamic storm‐enhanced density plume rapidly formed and decayed at local afternoon in the North American sector, with the plume intensity being doubled and halved in just a few hours. (c) The simultaneous occurrence of these strong yet opposite midlatitude gradient structures could be mainly attributed to common key drivers of prompt penetration electric fields and subauroral polarization stream electric fields. This shed light on the important role of storm‐time electrodynamic processes in shaping global ionospheric disturbances.

EUHFORIA modelling of the Sun-Earth chain of the magnetic cloud of 28 June 2013

Context. Predicting geomagnetic events starts with an understanding of the Sun-Earth chain phenomena in which (interplanetary) coronal mass ejections (CMEs) play an important role in bringing about intense geomagnetic storms. It is not always straightforward to determine the solar source of an interplanetary coronal mass ejection (ICME) detected at 1 au. Aims. The aim of this study is to test by a magnetohydrodynamic (MHD) simulation the chain of a series of CME events detected from L1 back to the Sun in order to determine the relationship between remote and in situ CMEs. Methods. We analysed both remote-sensing observations and in situ measurements of a well-defined magnetic cloud (MC) detected at L1 occurring on 28 June 2013. The MHD modelling is provided by the 3D MHD European Heliospheric FORecasting Information Asset (EUHFORIA) simulation model. Results. After computing the background solar wind, we tested the trajectories of six CMEs occurring in a time window of five days before a well-defined MC at L1 that may act as the candidate of the MC. We modelled each CME using the cone model. The test involving all the CMEs indicated that the main driver of the well-defined, long-duration MC was a slow CME. For the corresponding MC, we retrieved the arrival time and the observed proton density. Conclusions. EUHFORIA confirms the results obtained in the George Mason data catalogue concerning this chain of events. However, their proposed solar source of the CME is disputable. The slow CME at the origin of the MC could have its solar source in a small, emerging region at the border of a filament channel at latitude and longitude equal to +14 degrees.

A Framework for Evaluating Geomagnetic Indices Forecasting Models

AbstractThe use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capture the specific particularities of geomagnetic storms forecasting. Particularly, they do not provide insights during the high activity periods. To overcome this issue, we introduce the Binned Forecasting Error to provide a more accurate assessment of models' performance across the different intensity levels of a geomagnetic storm. This metric facilitates a robust comparison of different forecasting models, presenting a true representation of a model's predictive capabilities while being resilient to different storms duration. In this direction, for enabling fair comparison among models, it is important to standardize the sets of geomagnetic storms for model training, validation and testing. To do this, we have started from the current sets used in the literature for forecasting the SYM‐H, enriching them with newer storms not considered previously, focusing not only on disturbances caused by Coronal Mass Ejections but also addressing High‐Speed Streams. To operationalize the evaluation framework, a comparative study is conducted between a baseline neural network model and a persistence model, showcasing the effectiveness of the new metric in evaluating forecasting performance during intense geomagnetic storms. Finally, we propose the use of preliminary measurements from ACE to evaluate the model performance in settings closer to an operational real‐time scenario, where the forecasting models are expected to operate.

Climatology of the Elevated Mixed Layer over the Contiguous United States and Northern Mexico Using ERA5: 1979–2021

Abstract Elevated mixed layers (EMLs) influence the severe convective storm climatology in the contiguous United States (CONUS), playing an important role in the initiation, sustenance, and suppression of storms. This study creates a high-resolution climatology of the EML to analyze variability and potential changes in EML frequency and characteristics for the first time. An objective algorithm is applied to ERA5 to detect EMLs, defined in part as layers of steep lapse rates (≥8.0°C km−1) at least 200 hPa thick, in the CONUS and northern Mexico from 1979 to 2021. EMLs are most frequent over the Great Plains in spring and summer, with a standard deviation of 4–10 EML days per year highlighting sizable interannual variability. Mean convective inhibition associated with the EML’s capping inversion suggests many EMLs prohibit convection, although—like nearly all EML characteristics—there is considerable spread and notable seasonal variability. In the High Plains, statistically significant increases in EML days (4–5 more days per decade) coincide with warmer EML bases and steeper EML lapse rates, driven by warming and drying in the low levels of the western CONUS during the study period. Additionally, increases in EML base temperatures result in significantly more EML-related convective inhibition over the Great Plains, which may continue to have implications for convective storm frequency, intensity, severe perils, and precipitation if this trend persists. Significance Statement Elevated mixed layers (EMLs) play a role in the spatiotemporal frequency of severe convective storms and precipitation across the contiguous United States and northern Mexico. This research creates a detailed EML climatology from a modern reanalysis dataset to uncover patterns and potential changes in EML frequency and associated meteorological characteristics. EMLs are most common over the Great Plains in spring and summer, but show significant variability year-to-year. Robust increases in the number of days with EMLs have occurred since 1979 across the High Plains. Lapse rates associated with EMLs have trended steeper, in part due to warmer EML base temperatures. This has resulted in increasing EML convective inhibition, which has important implications for regional climate.

Effects of Background Synoptic Environment in Controlling South China Sea Tropical Cyclone Intensity and Size Changes in Pseudo–Global Warming Experiments

Abstract Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC’s R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions.

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Abstract. Tropical-cyclone impacts can have devastating effects on the population, infrastructure, and natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed and/or intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound-flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of &lt;10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecasting the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results 3 d before landfall (&lt;1 % flood probability error and &lt;20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.

How Solar Wind Controls the Recovery Phase Morphology of Intense Magnetic Storms

AbstractGeomagnetic storms are critical space weather phenomena resulting from the interaction between the solar wind and the Earth's magnetosphere. However, most studies focus on the main phase of magnetic storms, leaving the morphology of the recovery phase an open question. In this study, we analyze 82 intense magnetic storms with the minimum Dst index ≤ −100 nT between 1995 and 2018, finding that these storms can be classified into two distinct types: one‐stage recovery storms that exhibit a single rapid exponential recovery and two‐stage recovery storms that are characterized by a rapid exponential recovery in the early recovery phase and a slow linear recovery in the later recovery phase. We find that the two‐stage recovery storms are dominant, accounting for approximately 60% of the events. Interestingly, the proportion of two‐stage recovery storms peaks during solar minimum. The two‐stage recovery storms tend to be accompanied by more Alfvén waves with long‐duration and intense southward interplanetary magnetic fields. In addition, we find that the decay rate of the Dst index in the later recovery phase is correlated with the average BZ of the interplanetary magnetic field when the solar wind has a high degree of Alfvénicity. Overall, our results shed new light on the recovery phase morphology of intense magnetic storms and highlight the role of Alfvén waves in this process.

Historical and contemporary drivers of knickpoint retreat and morphological evolution along Bayou Pierre, Mississippi

AbstractChannel incision in rivers can cause marked ecological and economic damages. This phenomenon is abundant and generally well documented along impacted streams in the loess hills adjacent to the Lower Mississippi River valley. Bayou Pierre, an ecologically important small tributary of the Mississippi River, is currently incising but causes are not well understood. In this study, we examine diverse data sources to: (1) reconstruct a history of erosional stimuli and possible origins and (2) examine effects of contemporary controls. Review of long‐term planform and land use data for the Mississippi River revealed episodic foreshortening events followed by episodic deforestation and reforestation. Hydrologic data suggest an increase in rainfall over the last few decades. Estimates of knickpoint retreat place origins prior to Mississippi River channel straightening (1929–1944). Planform analysis in three focal reaches of Bayou Pierre demonstrates slow change prior to 1982, but accelerated changes after those periods. Mean rainfall and 3‐day storm intensity correlate to some planform changes; however, the storm of 1983 may be a better explanation of sudden planform change. We found some evidence of potential internal feedback loops in patterns of bar growth. Together, our analyses provide a synthesis of stimuli experienced by Bayou Pierre over the last ~200 years and suggest both channel migration events before straightening of the Mississippi River and more recent hydrologic events have influenced patterns of geomorphic change in Bayou Pierre.

Compounding effects of changing sea level and rainfall regimes on pluvial flooding in New York City

AbstractCoastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC’s assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC’s drainage models focusing on rainfall-induced flooding. We showed that the city’s stormwater system is highly vulnerable to high-intensity short-duration “cloudburst” events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC’s residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city.