Intense Storms Research Articles

Response of erosion-induced carbon loss to rainfall characteristics is forest type dependent

The paucity of studies on the response of erosion-induced carbon (C) loss to rainfall characteristics hinders our ability to estimate how changes in rainfall regime may affect C budget of forests, particularly in regions prone to water erosion. In the current study, we monitored the losses of dissolved organic carbon (DOC) and particulate organic carbon (POC) in relation to rainfall depth, average intensity, maximum 5 min intensity (I5), erosivity (EI30), and vegetation cover in three young forests during the first three years of reforestation. The three forest types include an assisted naturally regenerated (ANR) forest, a young Castanopsis carlessi plantation (YCP), and a young Cunninghamia lanceolata (Chinese fir) plantation (YCF). Our results indicate that POC was the dominant form of C loss, and erosion-induced C loss was lower in the ANR than the two plantations, likely due to its high vegetation cover. Erosion-induced C loss decreased through time across forest types as vegetation cover increased, highlighting the role of vegetation cover in mitigating C loss. The response of C loss to rainfall characteristics varied among different forest types. Vegetation cover and rainfall depth had similar contributions in predicting DOC flux in the ANR, while rainfall depth was more important in the YCP and vegetation cover was more important in the YCF. The most important factor predicting POC loss was vegetation cover in the ANR, I5 in the YCP, while in the YCF vegetation cover and I5 were of similar importance. Overall, DOC loss was mainly driven by rainfall depth, while POC loss was more related to I5. Our results suggest that projected increases in storm intensity associated with climate change are likely to increase erosion-induced C loss. And ANR should be widely promoted as an effective management practice of C conservation.

Study of the ionospheric responses over African and Asian longitudes to the intense geomagnetic storm of August 2018

Study of the ionospheric responses over African and Asian longitudes to the intense geomagnetic storm of August 2018

Forecast of Intense Geomagnetic Storms in February-March 2023, Based on Measurements of Galactic Cosmic Ray Intensity Fluctuations

Forecast of Intense Geomagnetic Storms in February-March 2023, Based on Measurements of Galactic Cosmic Ray Intensity Fluctuations

Geomagnetic Storm Effects on the LEO Proton Flux During Solar Energetic Particle Events

AbstractDuring a few solar energetic particle (SEP) events, solar protons were trapped within the geomagnetic field and reached the outer edge of the inner radiation belt. We reproduced this phenomenon by modeling the proton flux distribution at the Low‐Earth Orbit (LEO) for different geomagnetic conditions during solar particle events. We developed a three‐dimensional relativistic test particle simulation code to compute the 70–180 MeV solar proton Lorentz trajectories in low L‐shell range from 1 to 3. The Tsyganenko model (T01) generated the background static magnetic field with the IGRF (v12) model. We have selected three Dst index values: −7, −150, and −210 nT, to define quiet time, strong, and severe geomagnetic storms and to generate the corresponding inner magnetic field configurations. Our results showed that the simulated solar proton flux was more enhanced in the high‐latitude regions and more expanded toward the lower latitude range as long as the geomagnetic storm was intensified. Satellite observations and geomagnetic cutoff rigidities confirmed the numerical results. Furthermore, the LEO proton flux distribution was deformed, so the structure of the proton flux inside the South Atlantic Anomaly (SAA) became longitudinally extended as the Dst index decreased. Moreover, we have assessed the corresponding radiation environment of the LEO mission. We realized that, for a higher inclined LEO mission during an intense geomagnetic storm (Dst = −210 nT), the probability of the occurrence of the Single Event Upset (SEU) rates increased by 19% and the estimated accumulated absorbed radiation doses increased by 17% in comparison with quiet conditions.

Wave dissipation by vegetation for coastal defense in the Western Mekong Delta under climate change

The combination of the Mekong Delta dyke-mangrove system effectively protects the hinterland against flooding and erosion in extreme conditions for decades since they could dissipate waves and currents efficiently. However, literature and satellite data indicate that severe erosion happens along the Western sea-dykes of the Ca Mau coast caused by increased intensity of storms due to climate change. State-of-the-art wave transformation models are used to predict the influences of wave propagation and wave overtopping discharge over sea dykes and/or associated with mangrove ecosystems under extreme typhoons, which cooperates with both high – frequencies (HF) and low – frequencies (LF) waves. Results confirm that the HF wave quickly dissipates in shallow water, whereas the LF wave is dominant and penetrates over a longer distance. At dyke toe, the wave height through vegetation is substantially diminished, approximately ten times less than that in the sea-dyke solely. However, the modelling wave overtopping discharge seems to be overestimated compared with the standard-based formula.

Automatic sunspot detection through semantic and instance segmentation approaches

The solar influence on space weather and terrestrial environment is substantial. Strong geomagnetic storm activity can significantly affect astronauts in orbit, communications and GPS systems and disrupt Earth’s power distribution networks, making continuous monitoring and forecasting of solar activity vital. Sunspots are magnetic disturbances in the photosphere characterized by their dark appearance in the solar disk, being directly related to phenomena that contribute to these intense storms, namely solar flares and coronal mass ejections. This article lies at the intersection between solar surveillance and computer vision by applying state-of-the-art deep learning algorithms in the automatic detection of sunspots and sunspot groups. Based on two techniques, semantic segmentation and instance segmentation, two algorithms are implemented to tackle both purposes, U-Net and Mask R-CNN respectively. The ground-truth dataset was built from the available Debrecen Heliographic Observatory (DHO) space-borne sunspot catalogues from 2010 to 2014. The best U-Net implemented model presented a 74.2% IoU, surpassing the detection results evidenced by the Automated Solar Activity Prediction System (ASAP). The instance segmentation approach, a novelty application technique for sunspot group detection and still a challenging task in computer vision, achieved 51.7 bounding box AP and 78.6% accuracy in predicting the number of sunspot groups.

The Extreme Space Weather Event of 1872 February: Sunspots, Magnetic Disturbance, and Auroral Displays

We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman–Silverman storm) apparently originated from a complex active region of moderate area (≈ 500 μsh) that was favorably situated near disk center (S19° E05°). There is circumstantial evidence for an eruption from this region at 9–10 UT on 1872 February 3, based on the location, complexity, and evolution of the region, and on reports of prominence activations, which yields a plausible transit time of ≈29 hr to Earth. Magnetograms show that the storm began with a sudden commencement at ≈14:27 UT and allow a minimum Dst estimate of ≤ −834 nT. Overhead aurorae were credibly reported at Jacobabad (British India) and Shanghai (China), both at 19.°9 in magnetic latitude (MLAT) and 24.°2 in invariant latitude (ILAT). Auroral visibility was reported from 13 locations with MLAT below ∣20∣° for the 1872 storm (ranging from ∣10.°0∣–∣19.°9∣ MLAT) versus one each for the 1859 storm (∣17.°3∣ MLAT) and the 1921 storm (∣16.°2∣ MLAT). The auroral extension and conservative storm intensity indicate a magnetic storm of comparable strength to the extreme storms of 1859 September (25.°1 ± 0.°5 ILAT and −949 ± 31 nT) and 1921 May (27.°1 ILAT and −907 ± 132 nT), which places the 1872 storm among the three largest magnetic storms yet observed.

NO Radiative Cooling and Ionospheric Response to the HILDCAA Events Following Geomagnetic Storms

AbstractNitric oxide infrared (5.3 µm) radiative cooling plays an important role in Earth's upper atmospheric energy balance during space weather events. Its behavior during a HILDCAA event following a geomagnetic storm can be more complicated than what is observed in an isolated geomagnetic storm (without HILDCAA). In order to understand this contrast, two moderate (SYM‐Hmin −80 nT; April 2005 (event‐1) and −65 nT; December 2006 (event‐2)), and an intense (Dstmin −150 nT; May 2000 (event‐3)) geomagnetic storms followed by HILDCAA events are considered for this study. It is found that, despite the larger solar wind‐magnetospheric energy coupling during the main phases of event‐1 and event‐2, the NO radiative cooling (NORC) and energetic electron precipitation (EEP) rates are larger during the HILDCAA phase. TIE‐GCM simulations indicate that the NORC pattern can be mainly attributed to variation in temperature, [O], and [NO] during HILDCAA. A larger rates of EEP due to the continuous substorm activity contributed to an additional ionization in D‐region and E‐region of the northern polar ionosphere during HILDCAA period of event‐2. This work is the first attempt to understand the NORC and EEP fluctuations during HILDCAA events preceded by geomagnetic events like event‐1 and event‐2. This study presents a comprehensive investigation of NORC and energetic electron (>27.93 keV) precipitation rate during HILDCAA event preceded by a moderate geomagnetic storm. An atmospheric chemistry‐based NORC emission model is used to understand the variation of NORC during intense geomagnetic storm (event‐3).

Morphodynamics of two Mediterranean microtidal beaches presenting permanent megacusps under the influence of waves and strong offshore winds

This study focuses on four years of survey of two low-microtidal emerged beaches (average tidal range ≈ 0.3 m), presenting permanent megacusps on the French Mediterranean coast. These beaches are representative of the southern part of the Gulf of Lion (45 km of coast): the Leucate beach backed by a small dune and the La Franqui beach backed by a small lagoon. An analysis of the hydrodynamic and meteorological forcing along with morphological changes allows to define the key parameters controlling the system. These results are used to develop a conceptual model composed of five main situations and then compare it to the microtidal literature. Dominant offshore winds can reach speeds of up to 20 m s−1 (Gusts up to 30 m s−1) and have an erosive action on the backshore and produce advance of the shoreline of a few metres. This shoreline advance is mainly concentrated on the horns when it is concomitant with small fair-weather waves. Waves of moderate to high intensity (storms) lead to strong erosion and steepening of the beach face, as well as the migration or construction of a new higher berm located landward and associated with sediment deposition on the upper beach. The low tidal range may also allow the high run-up level to stabilise for a longer period of time in a high elevation on the backshore. The alternation of storm episodes and offshore wind periods could control the relative stability of the emerged beach.

Comparison of wave overtopping estimation models for urban beaches. Towards an early warning system on the Basque coast

This study compares the performance of different wave overtopping estimation models at urban beaches. The models selected for comparison are the Mase et al. (2013) and EurOtop parametric models and the XBeach process-based model in surfbeat and non-hydrostatic mode. Seven energetic storms are selected between 2015 and 2022 with offshore significant wave height ranging between 3 m and 8 m and peak period between 12 s and 20 s to perform the model comparison. The information required to run and validate the models (beach slope, shoreface shape, absence/presence of overtopping) was collected for each storm from coastal videometry. To account for the uncertainties derived from the incident waves randomness and the bathymetry shape when using the process-based model, a series of simulations with random seed boundary conditions were run over two different realistic profile shapes for each storm. The present study is a pilot study on the beach of Zarautz; however, it can be extended to other beaches of the Basque coast. Results indicate that while Mase et al. (2013) and EurOtop tend to reasonably predict the absence or presence of overtopping events, they tend to underestimate the hazard level at the beach of Zarautz. Additionally, the beach underwater profile shape can affect the process-based model performance at intermediate intensity storms and to a lesser extend during moderate storms. Finally, the hazard level at the beach of Zarautz varies significantly alongshore due to the configuration of the seawall, highlighting the need for local adaptation measures. Considering that there is no model that systematically performs better than others, it might be reasonable to use model assemble techniques to draw conclusions from a probabilistic perspective.

Effects of storms on fisheries and aquaculture: An Icelandic case study on climate change adaptation

ABSTRACT Climate change research on fisheries is often focused on changes in species abundance and distribution, yet the impacts of severe weather events are also important. Climate models indicate that storm frequency and intensity may increase in the North Atlantic; however, uncertainties remain and consequences in Iceland are not well studied. This research represents a first attempt to understand local to regional implications of storminess on Icelandic fisheries industries. Using an interdisciplinary approach, the analysis (1) provides regional future wind speed projections in Iceland and surroundings under the Shared Socioeconomic Pathway (SSP) 3–7.0 scenario, (2) documents current local experiences using exploratory interviews with various stakeholders in fisheries and aquaculture, and (3) explores research priorities for understanding climate-change adaptations in coastal communities. Projections show that whereas areas of the west, south, and east of Iceland may experience fewer storm days, the region north/northeast of Iceland features an increase in storm days. Potential adaptations include a flexible management system that does not punish fishers for lost time due to bad weather, cooperative market arrangements among sectors, and job security considerations for fishers and fish processing workers. Future projections are needed for other variables such as precipitation, and future socioeconomic studies should address the predictions and impacts of storminess.

Disentangling Dynamic from Thermodynamic Summer Ice Area Loss from Observations (1979–2021): A Potential Mechanism for a “First-Time” Ice-Free Arctic

Abstract In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.

Winter Storm Activity across Canada at the End of the Century: A CMIP5 Multi-model Projection

ABSTRACT Extratropical cyclones are the main source of precipitation in most of Canada. They bring hazardous weather events like blizzards, heavy snow, freezing rain, etc. They also supply fresh water through spring snow melt and maintain the soil and surface water balance in sub-humid and semi-arid regions. Any future changes in extratropical cyclone activity over Canada may have a significant impact on agriculture and the local economies of the various regions. Future changes in regional storm activity over Canada have not been studied in detail. Therefore, we examined potential changes in extratropical storm activity by the end of the century using data from six models from CMIP5 under historical and future (RCP 8.5) emission scenarios. A statistical analysis of selected storm activity indices, using a storm identification and tracking algorithm, revealed a decrease in the number of storms over the mid-latitude regions of Canada. However, intense storms with a longer duration are projected over all regions at the end of the century. A further investigation of the physical mechanisms revealed that a decrease in the meridional temperature gradient over the mid-latitude regions and shifting of the vertical wind shear are responsible for these expected changes in storm activity.

Identifying the Drivers of Caribbean Severe Weather Impacts

Severe weather impacts in the central Caribbean are quantified by an objective index of daily maximum wind and rainfall (W•R) in the area 16–19°N, 63–69°W over the period 1970–2021. The index, based on ERA5 hindcast assimilation of satellite and in situ data, peaks from the July to October season as high sea temperatures and weak wind shear promote tropical cyclogenesis. Climate forcing is studied by reducing the W•R index to seasonal values and regressing the time series onto reanalysis fields 10°S–25°N, 180°W–20°E. The outcome reflects Jul–Oct warming in the tropical Atlantic, cooling in the tropical east Pacific (cold tongue), decreased/increased convection over the Pacific/Atlantic, and tropical upper easterly winds. New findings emerge in the Mar–Jun season preceding higher W•R: reduced SW-cloud bands in the northeast Pacific, a convective trough over the equatorial Atlantic, and Caribbean cold-air outbreaks. The multivariate El Niño Southern Oscillation index correlates with Jul–Oct Caribbean W•R at 2-month lead time and shows growing influence. Composite analysis of the top-10 years identifies an anomalous Pacific–Atlantic Walker Circulation favoring higher Caribbean W•R. Salinity is below normal and heat flux is downward across the Atlantic. Anomalous low-level airflow inhibits upwelling in the SW Caribbean, deepening atmospheric moisture. A leading case (TC Fiona 2022) demonstrates the environmental conditions underpinning storm intensification. The key drivers of severe weather impacts yield guidance in strategic planning, risk management and disaster preparedness. New insights are gained from a localized index of severe weather.

Evaluation of IPCC Models’ Performance in Simulating Late-Twentieth-Century Weather Patterns and Extreme Precipitation in Southeastern China

Climate model evaluation work has made progress both in theory and practice, providing strong support for better understanding and predicting climate change. However, at the weather scale, there is relatively little assessment of climate models in terms of daily-scale climate phenomena, such as storm frequency and intensity. These weather-scale variables are of significant importance for our understanding of the impacts of climate change. In order to assess the capability of climate models to simulate weather-scale climate patterns, this study employs Self-Organizing Maps (SOMs) for weather pattern classification. By combining different evaluation metrics, varying the number of SOM types, changing the size of the study area, and altering the reference datasets, the climate models are evaluated to ensure the robustness of the assessment results. The results demonstrate that the size of the study area is positively correlated with observed differences, and there are correlations among different evaluation metrics. The highest correlation is observed between evaluation metrics in large-scale and small-scale spatial domains, while the correlation with SOM size is relatively low. This suggests that the choice of evaluation metrics has a minor impact on model ranking. Furthermore, when comparing the correlation coefficients calculated using the same evaluation metrics for different-sized regions, a significant positive correlation is observed. This indicates that variations in the size of the study area do not significantly affect model ranking. Further investigation of the relationship between model performance and different SOM sizes shows a significant positive correlation. The impact of dataset selection on model ranking is also compared, revealing high consistency. This enhances the reliability of model ranking. Taking into account the influence of evaluation metric selection, SOM size, and reanalysis data selection on model performance assessment, significant variations in model ranking are observed. Based on cumulative ranking, the top five models identified are ACCESS1-0, GISS-E2-R, GFDL-CM3, MIROC4h, and GFDL-ESM2M. In conclusion, factors such as evaluation metric selection, study area size, and SOM size should be considered when assessing model ranking. Weather pattern classification plays a crucial role in climate model evaluation, as it helps us better understand model performance in different weather systems, assess their ability to simulate extreme weather events, and improve the design and evaluation methods of model ensemble predictions. These findings are of great significance for optimizing and strengthening climate model evaluation methods and provide valuable insights for future research.

Magnetic Storm Effects on the Occurrence and Characteristics of Plasma Bubbles

AbstractThe Communications/Navigation Outages Forecast System satellite mission was designed to investigate the ionospheric conditions that lead to the formation of irregularities. Here, we have studied the effect of magnetic storms on the formation and evolution of plasma bubbles during the satellite's lifetime (2008–2015). During this period encompassing solar minimum and maximum conditions, many magnetic storms of varying intensity developed, producing a unique and rich data set of 248 storms (14 intense, 69 moderate, and 165 weak) that occurred during the same timeframe to examine the role of external magnetospheric drivers in the production and dynamics of equatorial plasma bubbles. We have used the Planar Langmuir Probe and Ion Velocity Meter instruments to elucidate the role of magnetic storm intensity on the bubble's depth, internal speed, width, occurrence, and lifetime. The pre‐reversal enhancement (PRE) tends to increase during the main phase and when BZ is southward. New bubbles occur during large excursions of the PRE value. The bubble lifetime extends and remains active during the main and part of the recovery phase. The plasma velocity within the bubbles increases and typically becomes over 100 m/s during significant PRE and BZ negative times. The depth of bubbles reaches values close to 100% during intense storms. In general, the intensity of the storms seems to control and augment the plasma bubbles' depth, width, and internal velocity.

Calm after the storm? Similar patterns of genetic variation in a riverine foundation species before and after severe disturbance.

In summer 2011, Tropical storms Lee and Irene caused an estimated 90% decline of the submersed aquatic plant Vallisneria americana Michx. (Hydrocharitaceae) in the Hudson River of New York (USA). To understand the genetic impact of such large-scale demographic losses, we compared diversity at 10 microsatellite loci in 135 samples collected from five sites just before the storms with 239 shoots collected from nine sites 4 years after. Although 80% of beds sampled in 2011 lacked V. americana in 2015, we found similar genotypic and genetic diversity and effective population sizes in pre-storm versus post-storm sites. These similarities suggest that despite local extirpations concentrated at the upstream end of the sampling area, V. americana was regionally resistant to genetic losses. Similar geographically based structure among sites in both sampling periods suggested that cryptic local refugia at previously occupied sites facilitated re-expansion after the storms. However, this apparent resistance to disturbance may lead to a false sense of security. Low effective population sizes and high clonality in both time periods suggest that V. americana beds were already small and had high frequency of asexual reproduction before the storms. Dispersal was not sufficient to recolonize more isolated sites that had been extirpated. Chronic low diversity and reliance on asexual reproduction for persistence can be risky when more frequent and intense storms are paired with ongoing anthropogenic stressors. Monitoring genetic diversity along with extent and abundance of V. americana will give a more complete picture of long-term potential for resilience.

The Variation of Ionospheric O+ and H+ Outflow on Storm Timescales

AbstractGeomagnetic storms are primarily driven by stream interaction regions (SIRs) and coronal mass ejections (CMEs). Since SIR and CME storms have different solar wind and magnetic field characteristics, the magnetospheric response may vary accordingly. Using FAST/TEAMS data, we investigate the variation of ionospheric O+ and H+ outflow as a function of geomagnetic storm phase during SIR and CME magnetic storms. The effects of storm size and solar EUV flux, including solar cycle and seasonal effects, on storm time ionospheric outflow, are also investigated. The results show that for both CME and SIR storms, the O+ and H+ fluences peak during the main phase, and then declines in the recovery phase. However, for CME storms, there is also significant increase during the initial phase. Because the outflow starts during the initial phase in CME storms, there is time for the O+ to reach the plasma sheet before the start of the main phase. Since plasma is convected into the ring current from the plasma sheet during the main phase, this may explain why more O+ is observed in the ring current during CME storms than during SIR storms. We also find that outflow fluence is higher for intense storms than moderate storms and is higher during solar maximum than solar minimum.

Morphological Performance of Vegetated and Non-Vegetated Coastal Dunes with Rocky and Geotextile Tube Cores under Storm Conditions

Coastal dune restoration projects are increasingly seen as a means of protecting the coast against storms, especially with the use of natural (plants), artificial (geotextile tube cores) and hybrid (plants and geotextile tube) elements. As geotextile tube cores have been found to negatively affect dune and beach natural morphodynamics, rocky cores are thus seen as a potential alternative. We carried out laboratory experiments to compare the performance of dunes with rocky cores, geotextile cores, and both types when coupled with planted vegetation. We investigated these elements in the context of scaled mild, moderate, and intense storm conditions. The results showed that dunes with either type of core lost more sand than dunes without cores. The addition of plants generally reduced the erosion across the various options, but most strongly for rocky cores under mild and moderate storm conditions. We also found that dunes with a high density of plants were best for intense conditions. Overall, the use of rocky dune cores, when coupled with plants, is the most suitable and sustainable alternative option in hybrid engineering projects.

Observations of Extreme Waves and Wave Spectra during Hurricane Dorian (2019)

Due to the sparse distribution of buoys in the ocean, direct observations of extreme wave activity as excited by tropical cyclones (TCs) are few; but nonetheless, these buoys provide rare opportunities to learn more about how weather systems interact with the ocean wave field. In this study, National Oceanic and Atmospheric Administration National Data Buoy Center buoy observations of one-dimensional (1D) and two-dimensional (2D) wave spectra during the translation speed of Hurricane Dorian (2019) through the Atlantic Ocean were used to examine extreme wave events in the days before and after its September 1, 2019 landfall in The Bahamas. Observations of wave properties during storm intensification and decay showed that although significant wave height naturally rose and fell, the dominant wave period remained virtually constant. At the height of the storm, a lethal combination formed, with the highest significant wave heights being recorded at over 8 m (more than four times the mean) and dominant wave periods exceeded 12 s. Wave direction also varied widely as wave regimes shifted from wind-sea to swell, with spectral wave energy peaking at over 120 m2/Hz, six times higher than pre-Hurricane Dorian wave states. This study provides the first in situ characterization of extreme wave heights as induced by Hurricane Dorian. The study recommends that a number of observational platforms be funded, developed, and deployed throughout the Lucayan Archipelago.