Space Weather Modeling Framework Research Articles

LiveWire: Horizontal Geoelectric Field Prediction With 1‐hr Lead‐Time Using Multi‐Fidelity Boosted Neural Networks

AbstractGeomagnetically Induced Currents (GICs) are electrical currents generated by rapid changes in the geomagnetic field during space weather events, posing risks to power grids and pipelines. Traditional approaches predict GICs indirectly by forecasting , the temporal variation of the geomagnetic field, which is proportional to the induced electric field via Faraday's law. However, current physics‐based models driven by in situ solar wind measurements offer only 10–30 min lead times, insufficient for power grid operators to take mitigating actions. Additionally, grid operators prefer direct forecasts of the geoelectric field, which directly influences GICs, rather than relying on intermediate predictions of that require complex and time‐consuming calculations. We present a novel approach that directly forecasts the horizontal geoelectric field with a one‐hour lead time, bypassing predictions. Our method combines magnetometer data, magnetotelluric survey data, and solar wind inputs into a new probabilistic multi‐fidelity machine learning technique, ProBoost, resulting in the LiveWire model. Using data from the Boulder Geomagnetic Observatory (BOU) since 2002, we trained and validated LiveWire on the top 50 geoelectric field events during geomagnetic storms. Our results show that LiveWire outperforms both a persistence forecast and the operational Space Weather Modeling Framework (SWMF) by at least 31% and 23%, respectively. This advancement in geoelectric field forecasting promises more accurate GIC predictions, helping enhance the resilience of critical infrastructure to space weather.

Extended metric validation of a semi-physical Space Weather Modeling Framework conductance model on field-aligned current estimations

In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by ∼12.7% and ∼6.24% before the storm, ∼4.52% and ∼2.13% better during the main phase, ∼1.98% and ∼1.27% worse during the second minimum, and better by ∼1.84% and ∼1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance.

Magnetospheric Time‐History in Storm‐Time Magnetic Flux Dynamics: A Global Simulation Campaign

AbstractThis paper aims to quantify the magnetospheric magnetic flux contents under moderate to intense space weather conditions using global simulations. This study is a companion to Akhavan‐Tafti, Atilaw, et al. (2023, https://doi.org/10.1029/2023JA031832) where magnetic flux evolution is presented for a catalog of storm events, using Heliophysics System Observatory (HSO) observations. For this study, we used the Space Weather Modeling Framework (SWMF) in Geospace configuration to study magnetic flux dynamics for a subset of their storm events (15 events). Simulations reliably resolve the storm‐time magnetic flux Bz and current density |J| asymmetries across the different storm phases. It is revealed that: relative to the quiet period, flux content is enhanced during the storm sudden commencement (SSC) phase in the dayside by ΔBz/Bz, quiet = +17%, and reduced in the nightside magnetosphere (r[RE] < −6 RE) by −15%. At the same time, the cross‐tail current is found to enhance (|J| = 2 nA/m2), which suggests the storm impact in the nightside magnetosphere is much earlier in the storm cycle than previously shown. Concurring with previous studies, a significant depletion of magnetic flux by up to −40%, with day‐night and dawn‐dusk asymmetries, can be seen during the main and recovery phases. This corresponds to the enhanced current density (|J| = 5–8 nA/m2) at ∼6 RE further confirming the role of ring current in driving magnetospheric dynamics during the main and recovery phases. This is in contrast with the SSC phase wherein the Chapman‐Ferraro and cross‐tail currents are the dominant current systems.

Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics

AbstractOne of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere‐ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high‐resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics‐based.

Accuracy of Global Geospace Simulations: Influence of Solar Wind Monitor Location and Solar Wind Driving

AbstractSome space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM‐H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM‐H depends on the spacecraft distance from the Sun‐Earth line. We also quantify the dependence of the standard deviation in SYM‐H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.

Quantifying the ability of magnetohydrodynamic models to reproduce observed Birkeland current and auroral electrojet magnitudes

Although global magnetohydrodynamic (MHD) models have increased in sophistication and are now at the forefront of modeling Space Weather, there is still no clear understanding of how well these models replicate the observed ionospheric current systems. Without a full understanding and treatment of the ionospheric current systems, global models will have significant shortcomings that will limit their use. In this study we focus on reproducing observed seasonal interhemispheric asymmetry in ionospheric currents using the Space Weather Modeling Framework (SWMF). We find that SWMF does reproduce the linear relationship between the electrojets and the FACs, despite the underestimation of the currents’ magnitudes. Quantitatively, we find that at best SWMF is only capturing approximately 60% of the observed current. We also investigate how varying F10.7 effects the ionospheric potential and currents during the summer and winter. We find that simulations ran with higher F10.7 result in lower ionospheric potentials. Additionally, we find that the models do not always replicate the expected behavior of the currents with varying F10.7. This work points to a needed improvement in ionospheric conductance models.

Comparing magnetopause predictions from two MHD models during a geomagnetic storm and a quiet period

Magnetopause location is an important prediction of numerical simulations of the magnetosphere, yet the models can err, either under-predicting or over-predicting the motion of the boundary. This study compares results from two of the most widely used magnetohydrodynamic (MHD) models, the Lyon–Fedder–Mobarry (LFM) model and the Space Weather Modeling Framework (SWMF), to data from the GOES 13 and 15 satellites during the geomagnetic storm on 22 June 2015, and to THEMIS A, D, and E during a quiet period on 31 January 2013. The models not only reproduce the magnetopause crossings of the spacecraft during the storm, but they also predict spurious magnetopause motion after the crossings seen in the GOES data. We investigate the possible causes of the over-predictions during the storm and find the following. First, using different ionospheric conductance models does not significantly alter predictions of the magnetopause location. Second, coupling the Rice Convection Model (RCM) to the MHD codes improves the SWMF magnetopause predictions more than it does for the LFM predictions. Third, the SWMF produces a stronger ring current than LFM, both with and without the RCM and regardless of the LFM spatial resolution. During the non-storm event, LFM predicts the THEMIS magnetopause crossings due to the southward interplanetary magnetic field better than the SWMF. Additionally, increasing the LFM spatial grid resolution improves the THEMIS predictions, while increasing the SWMF grid resolutions does not.

Recirculation of plasmasphere material during idealized magnetic storms

The fate of flux tube material once it is eroded from of the plasmasphere through a dayside plume remains unknown. The eroded plasmasphere material can be either swept away by the solar wind and lost from Earth’s system, or recirculated into the inner magnetosphere. Recirculating plasmasphere material could plausibly enter the central plasma sheet and contribute to the ring current. This work uses numerical models to explore this possibility. Historically this has been a difficult question to answer due to the fact that solar wind, ionosphere, and plasmaspheric plasmas are all dominated by hydrogen making it difficult to distinguish the source of plasma from observation alone. Recent advances in computing have enabled us to answer this question. Using the Space Weather Modeling Framework (SWMF) to couple the Block-Adaptive-Tree-Solar-Roe-Up-Wind-Scheme (BATS-R-US), Dynamic Global Core Plasma Model (DGCPM), and the Ridley Ionosphere Model (RIM), we can track the motion of the plasmaspheric material once it leaves the plasmasphere in a self-consistent manner.

The predictive power of magnetospheric models for estimating ground magnetic field variation in the United Kingdom

Space weather events can have damaging effects on ground-based infrastructure. Geomagnetically induced currents (GIC) caused by rapid magnetic field fluctuations during geomagnetic storms can negatively affect power networks, railways as well as navigation systems. To reduce such negative impacts, good forecasting capability is essential. In this study we assess the performance of contemporary magnetohydrodynamic (MHD) models in predicting the external-only ground magnetic field perturbations at three United Kingdom observatories during two severe space weather events: September 2017 and March 2015. Simulated magnetic data were acquired via Community Coordinated Modeling Center (CCMC), using the following models: Space Weather Modeling Framework (SWMF), Open Geospace General Circulation Model (Open GGCM) and Lyon–Fedder–Mobarry (LFM) combined with the Rice Convection Model (RCM). All simulations use spacecraft measurements at L1 as their solar wind input in calculating ground perturbations. Qualitative and quantitative comparison between measured and modelled values suggest that the performance of MHD models vary with latitude, the magnetic component and the characteristics of the storm analysed. Most models tend to exaggerate the magnitude of disturbances at lower latitudes but better capture the fluctuations at the highest latitude. For the two storms investigated, the addition of RCM tends to result in overestimation of the amplitude of ground perturbations. The observed data-model discrepancies most likely arise due to the many approximations required in MHD modelling, such as simplified solar wind input or shift in location of the electrojets in the simulated magnetospheric and ionospheric currents. It was found that no model performs consistently better than any other, implying that each simulation forecasts different aspects of ground perturbations with varying level of accuracy. Ultimately, the decision of which model is most suitable depends on specific needs of the potential end user.

Comparing the Performance of a Solar Wind Model from the Sun to 1 au Using Real and Synthetic Magnetograms

The input of the solar wind models plays a significant role in accurate solar wind predictions at 1 au. This work introduces a synthetic magnetogram produced from a dynamo model as an input for magnetohydrodynamics simulations. We perform a quantitative study that compares the space weather modeling framework (SWMF) results for the observed and synthetic solar magnetogram input. For each case, we compare the results for extreme ultraviolet images and extract the simulation data along the Earth trajectory to compare with in situ observations. We initialize the SWMF using the real and synthetic magnetograms for a set of Carrington rotations within solar cycles 23 and 24. Our results help quantify the ability of dynamo models to be used as input to solar wind models and thus provide predictions for the solar wind at 1 au.

Simulating the Responses of the Magnetosphere–Ionosphere System to the IMF By Reversal

The interplanetary magnetic field (IMF) By component can cause asymmetric features in the magnetosphere-ionosphere system and influence the electromagnetic energy input. In this study, we use the Space Weather Modeling Framework (SWMF) to study the transient dynamics after a sudden change in the IMF By component exerts on the magnetosphere system. Simulation results reveal that under northward IMF conditions, an abrupt change of the IMF By from duskward to dawnward induces a dawnward geomagnetic field firstly in the cusp region and then in both the near-Earth and magnetotail regions. The signal of the new y-component of the geomagnetic field in the near-Earth region extends tailward while that in the magnetotail moves Earthward, leading the middle-tail region to react last. We investigate in detail the transitional change of the y-component of the geomagnetic field and compare it to the scenario under southward IMF conditions, and find that the latter case reacts in a much simpler manner and the responses are faster globally.

Stormtime Energetics: Energy Transport Across the Magnetopause in a Global MHD Simulation

Coupling between the solar wind and magnetosphere can be expressed in terms of energy transfer through the separating boundary known as the magnetopause. Geospace simulation is performed using the Space Weather Modeling Framework (SWMF) of a multi-ICME impact event on February 18–20, 2014 in order to study the energy transfer through the magnetopause during storm conditions. The magnetopause boundary is identified using a modified plasma β and fully closed field line criteria to a downstream distance of −20Re. Observations from Geotail, Themis, and Cluster are used as well as the Shue 1998 model to verify the simulation field data results and magnetopause boundary location. Once the boundary is identified, energy transfer is calculated in terms of total energy flux K, Poynting flux S, and hydrodynamic flux H. Surface motion effects are considered and the regional distribution of energy transfer on the magnetopause surface is explored in terms of dayside X>0, flank X<0, and tail cross section X=Xmin regions. It is found that total integrated energy flux over the boundary is nearly balanced between injection and escape, and flank contributions dominate the Poynting flux injection. Poynting flux dominates net energy input, while hydrodynamic flux dominates energy output. Surface fluctuations contribute significantly to net energy transfer and comparison with the Shue model reveals varying levels of cylindrical asymmetry in the magnetopause flank throughout the event. Finally existing energy coupling proxies such as the Akasofu ϵ parameter and Newell coupling function are compared with the energy transfer results.

Modeling the Geomagnetic Response to the September 2017 Space Weather Event Over Fennoscandia Using the Space Weather Modeling Framework: Studying the Impacts of Spatial Resolution

AbstractWe must be able to predict and mitigate against geomagnetically induced current (GIC) effects to minimize socio‐economic impacts. This study employs the space weather modeling framework (SWMF) to model the geomagnetic response over Fennoscandia to the September 7–8, 2017 event. Of key importance to this study is the effects of spatial resolution in terms of regional forecasts and improved GIC modeling results. Therefore, we ran the model at comparatively low, medium, and high spatial resolutions. The virtual magnetometers from each model run are compared with observations from the IMAGE magnetometer network across various latitudes and over regional‐scales. The virtual magnetometer data from the SWMF are coupled with a local ground conductivity model which is used to calculate the geoelectric field and estimate GICs in a Finnish natural gas pipeline. This investigation has lead to several important results in which higher resolution yielded: (1) more realistic amplitudes and timings of GICs, (2) higher amplitude geomagnetic disturbances across latitudes, and (3) increased regional variations in terms of differences between stations. Despite this, substorms remain a significant challenge to surface magnetic field prediction from global magnetohydrodynamic modeling. For example, in the presence of multiple large substorms, the associated large‐amplitude depressions were not captured, which caused the largest model‐data deviations. The results from this work are of key importance to both modelers and space weather operators. Particularly when the goal is to obtain improved regional forecasts of geomagnetic disturbances and/or more realistic estimates of the geoelectric field.

Estimating Maximum Extent of Auroral Equatorward Boundary Using Historical and Simulated Surface Magnetic Field Data

AbstractThe equatorward extent of the auroral oval, the region which separates the open‐field polar cap regions with the closed field subauroral regions, is an important factor to take into account when assessing the risk posed by space weather to ground infrastructure. During storms, the auroral oval is known to move equatorward, accompanied by ionospheric current systems and significant magnetic field variations. Here we outline a simple algorithm which can be used to estimate the maximum extent of the auroral equatorward boundary (MEAEB) using magnetic field data from ground‐based observatories. We apply this algorithm to three decades of INTERMAGNET data, and show how the auroral oval in the Northern hemisphere moves South with larger (more negative Dst) storms. We simulate a number of storms with different magnitudes using the Space Weather Modeling Framework (SWMF), and apply the same auroral boundary detection algorithm. For SWMF simulated storms with Dst > −600nT, the estimates of the MEAEB are broadly in line with the same estimates for historical events. For the extreme scaled storms (with Dst < −1,000 nT), there is considerable scatter in the estimated location of the auroral equatorward boundary. Our largest storm simulation was calculated using Carrington‐like estimates for the solar wind conditions. This resulted in a minimum Dst = −1,142 nT, and a minimum estimated auroral boundary of 35.5° MLAT in places.

Aurora Sightings Observed in Chinese History Caused by CIRs or Great-storm CMEs

Abstract Auroras observed at middle and low geographic latitudes are related to external inputs and varying geomagnetic fields. This work aims to exclude corotating interaction region (CIR) storms and identify strong coronal mass ejection (CME) storms according to historical auroral records when the geomagnetic field varies substantially. An existing catalog of the aurora records in Chinese history reported by Zeng & Jin from 193 B.C. to 1911 A.D. is used. Archaeomagnetic field models are adopted to estimate the variation of the dipole field. According to the empirical relation between the equatorward boundary of the auroral oval, Dst index, and geomagnetic field intensity, the auroras caused by CIRs can be excluded, and those caused by strong CMEs are identified. After 1500 A.D., China’s magnetic latitude decreased substantially due to the pole shift. This shift provides a better opportunity to investigate the existence of great-level storms. These great-storm CMEs occurred in both solar maximum and minimum. The space weather modeling framework is used to calculate the cusp area and the downward ion flux through the cusp for varied geomagnetic field and solar wind. For the present solar wind condition and tilt angle <15°, stronger geomagnetic field tends to generate a larger cusp area and higher ion flux through the cusp. For the weaker solar wind in the Maunder minimum, the ion flux is lower, but the cusp area is similar to that at present.

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Magnetohydrodynamics (MHD)-based global space weather models have mostly been developed and maintained at academic institutions. While the “free spirit” approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).

Formation of the Low‐Energy “Finger” Ion Spectral Structure Near the Inner Edge of the Plasma Sheet

AbstractWe present a case study of the H+, He+, and O+ low‐energy “finger” structure observed by the Van Allen Probe A Helium, Oxygen, Proton, and Electron (HOPE) spectrometer on 26 October 2016. This structure, whose characteristic energy is from approximately tens of eV to a few keV, looks like a “finger” that is rich in O+ and He+, faint in H+ on an energy‐time spectrogram. By using the Space Weather Modeling Framework (SWMF) and Weimer05 electric fields, combined with a dipole or more self‐consistent magnetohydrodynamic (MHD) magnetic field, backward tracing of O+ reveals that the structure is formed by ions with a long drift time from the plasma sheet during the magnetic storm main phase to the inner region with trajectories dominated by eastward drift motion, and the formation depends on the convection electric field model. The heavy ion dominance of the feature is explained by charge exchange losses along the long slow drift paths.

Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts

AbstractIonospheric conductance is a crucial factor in regulating the closure of magnetospheric field‐aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere‐ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first‐principles‐based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere‐ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field‐aligned current values. CMEE has been developed using nonlinear regression over a year's worth of 1‐min resolution output from assimilative maps, specifically including times of extreme driving of the solar wind‐magnetosphere‐ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground‐based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents, which impact the simulated dB/dt and ΔB, resulting in substantial improvements in dB/dt predictive skill.

Including the Temporal Dimension in the SECS Technique

AbstractThe equivalent source method of Spherical Elementary Current Systems (SECS) has contributed valuable results for spatial magnetic interpolation purposes where no observations are available, as well as for modeling equivalent currents both in the ionosphere and in the subsurface, thus providing a separation between external and internal sources. It has been successfully applied to numerous Space Weather (SW) events, whereas some advantages have been reported over other techniques such as Fourier or Spherical (Cap) Harmonic Analysis. Although different modalities of SECS exist (either 1‐D, 2‐D, or 3‐D) depending on the number of space dimensions involved, the method provides a sequence of instantaneous pictures of the source current. We present an extension of SECS consisting in the introduction of a temporal dependence in the formulation based on a cubic B‐splines expansion. The technique thus adds one dimension, becoming 4‐D in general (e.g., 3‐D + t), and its application is envisaged for, though not restricted to, the analysis of past events including heterogeneous geomagnetic data sets, such as those containing gaps, different sampling rates or diverse data sources. A synthetic model based on the SW Modeling Framework is used to show the efficacy of the extended scheme. We apply this method to characterize the current systems of past and significant SW events producing geomagnetically induced currents, which we exemplify with an outstanding geomagnetic sudden commencement occurred on 24 March 1991.

Reconnection‐Driven Dynamics at Ganymede's Upstream Magnetosphere: 3‐D Global Hall MHD and MHD‐EPIC Simulations

AbstractThe largest moon in the solar system, Ganymede, is the only moon known to possess a strong intrinsic magnetic field and a corresponding magnetosphere. Using the latest version of Space Weather Modeling Framework (SWMF), we study the upstream plasma interactions and dynamics in this sub‐Alfvénic system. Results from the Hall magnetohydrodynamics (MHD) and the coupled MHD with embedded particle‐in‐cell (MHD‐EPIC) models are compared. We find that under steady upstream conditions, magnetopause reconnection occurs in a nonsteady manner, and the energy partition between electrons and ions is different in the two models. Flux ropes of Ganymede's radius in length form on the magnetopause at a rate about 3 min and create spatiotemporal variations in plasma and field properties. Upon reaching proper grid resolutions, the MHD‐EPIC model can resolve both electron and ion kinetics at the magnetopause and show localized nongyrotropic behavior inside the diffusion region. The estimated global reconnection rate from the models is about 80 kV with 60% efficiency, and there is weak evidence of ∼1 min periodicity in the temporal variations due to the dynamic reconnection process.