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Rapid plasma eruptions explosively release energy within Earth’s magnetosphere, at the Sun and at other planets. At Earth, these eruptions, termed plasmoids, occur in the magnetospheric nightside and are associated with sudden brightening of the aurora. The chain of events leading to the plasmoid is one of the longest-standing unresolved questions in space physics. Two competing paradigms have been proposed to explain the course of events. The first asserts that magnetic reconnection changes the magnetic topology in the tail, severing a part of the magnetosphere as plasmoid. The second employs kinetic instabilities that first disrupt the current sheet supporting the magnetotail and launch waves that trigger the topological change to eject the plasmoid. Here we numerically simulate Earth’s magnetosphere at realistic scales using a model that captures the physics underlying both paradigms. We show that both magnetic reconnection and kinetic instabilities are required to induce a global topological reconfiguration of the magnetotail, thereby combining the seemingly contradictory paradigms. Our results help to understand how plasma eruptions may take place, guide spacecraft constellation mission design to capture these ejections in observations and lead to improved understanding of space weather by improving the predictability of the plasmoids.Both magnetic reconnection and kinetic instabilities are required to produce magnetotail plasma eruptions, according to high-resolution global simulations of Earth’s magnetosphere.

Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals.

Various space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010–2014) and the Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission Solar and Heliospheric Observatory , which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a manual process based on assumed knowledge of the sensor hardware. Here we present a new data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e - 0.17 ± 0.29  W/m 2 ) between the TSI levels during the two most recent solar minima in 2008/09 and 2019/20. The new algorithm can be applied to any TSI experiment that employs a multi-channel philosophy for degradation tracking. It does not require deep technical knowledge of the individual radiometers.

A strong geomagnetic storm that occurred on 01-December-2023 triggered unusual equatorial plasma bubbles (EPB) over 100-140°E longitudes, which persisted for several hours after sunrise on the next day. FORMOSAT-7/COSMIC-2 and ground-based global navigation satellite system observations, and Global Ionospheric Specification (GIS) electron density are used to investigate this long-lasting unseasonal EPB episode in the solstice period over Asia-Pacific. The results show that in presence of elevated F-layer bottom-side aided by prompt penetration electric field (PPEF), large-scale traveling ionospheric disturbances (LSTID) generated by high-latitude Joule heating seeded the instability soon after sunset. However, it is a second phase of reinforced EPBs generated in the post-midnight period triggered by another group of larger LSTIDs that uncharacteristically prolonged into daytime hours. The GIS observations further provide evidence that the extremely low background ionization on the following day due to composition changes during the negative storm phase enabled these EPBs to survive even after sunrise before the depleted flux tubes were refilled by fresh ionization. The coordinated ground- and space-based observations demonstrate the causal links for the rare unseasonal EPBs occurring in the post-sunset and post-midnight periods over the same longitude sector and the latter persisting several hours after sunrise with potentially enduring space weather implications on satellite communication and navigation.

The variable solar cycle of activity is a long-standing problem in physics. It modulates the overall level of space weather activity at earth, which in turn can have significant societal impact. The Hilbert transform of the sunspot number is used to map the variable length, approximately 11 year Schwabe cycle onto a uniform clock. The clock is used to correlate extreme space weather seen in the aa index, the longest continuous geomagnetic record at earth, with the record of solar active region areas and latitudes since 1874. This shows that a clear switch-off of the most extreme space weather events occurs when > 90 % of solar active region areas have moved to within about 15° of the solar equator, from regions of high gradient in solar differential rotation which can power coronal mass ejections, to a region where solar differential rotation is almost constant with latitude. More moderate space weather events which coincide with 27 day solar rotation recurrences in the aa index, consistent with stable, persistent source regions of high speed streams, commence when the centroid of solar active region areas moves to within 15° of the solar equator. This offers a physical explanation for the longstanding identification of a two component cycle of activity in the aa index.

Nitrogen is a critical ingredient of complex biological molecules. Molecular nitrogen, however, which was outgassed Into the Earth's early atmosphere, is relatively chemically inert and nitrogen fixation into more chemically reactive compounds requires high temperatures. Possible mechanisms of nitrogen fixation include lightning, atmospheric shock heating by meteorites, and solar ultraviolet radiation. Here we show that nitrogen fixation in the early terrestrial atmosphere can be explained by frequent and powerful coronal mass ejection events from the young Sun -- so-called superflares. Using magnetohydrodynamic simulations constrained by Kepler Space Telescope observations, we find that successive superflare ejections produce shocks that accelerate energetic particles, which would have compressed the early Earth's magnetosphere. The resulting extended polar cap openings provide pathways for energetic particles to penetrate into the atmosphere and, according to our atmospheric chemistry simulations, initiate reactions converting molecular nitrogen, carbon dioxide and methane to the potent greenhouse gas nitrous oxide as well as hydrogen cyanide, an essential compound for life. Furthermore, the destruction of N2, C02 and CH, suggests that these greenhouse gases cannot explain the stability of liquid water on the early Earth. Instead, we propose that the efficient formation of nitrous oxide could explain a warm early Earth.

The solar corona is shaped and mysteriously heated to millions of degrees by the Sun’s magnetic field. It has long been hypothesized that the heating results from a myriad of tiny magnetic energy outbursts called nanoflares, driven by the fundamental process of magnetic reconnection. Misaligned magnetic field lines can break and reconnect, producing nanoflares in avalanche-like processes. However, no direct and unique observations of such nanoflares exist to date, and the lack of a smoking gun has cast doubt on the possibility of solving the coronal heating problem. From coordinated multi-band high-resolution observations, we report on the discovery of very fast and bursty nanojets, the telltale signature of reconnection-based nanoflares resulting in coronal heating. Using state-of-the-art numerical simulations, we demonstrate that the nanojet is a consequence of the slingshot effect from the magnetically tensed, curved magnetic field lines reconnecting at small angles. Nanojets are therefore the key signature of reconnection-based coronal heating in action. Multi-band high-resolution observations reveal very fast and bursty nanojets. These nanojets are a consequence of the slingshot effect from magnetically tensed, curved magnetic field lines reconnecting at small angles, resulting in coronal heating.

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.

Galactic Cosmic Rays (GCRs) are charged particles, originating from galactic and/or extra-galactic Supernova Remnants (SNR), that continuously permeate the Heliosphere. The GCRs are modulated in the heliosphere by convection by solar wind (SW), drift via gradients and curvatures in the Heliospheric Magnetic Field (HMF), diffusion from fluctuations in the HMF, and adiabatic cooling in the expanding SW. An improved understanding of their modulation is imperative as studies on the variations in solar activity levels and solar eruptions in the past rely heavily on the relationship between their modulation and formation of the secondary particles in the Earth’s atmosphere. Here, for the first time, we utilize an AI method, Light Gradient Boosting Machines (LightGBM), to investigate the nonlinear interplay among the modulation processes in different timescales. Our study indicates that the nonlinear interplay among the mechanisms responsible for the GCR modulation in the inner heliosphere are not limited to the scenario of “drift-dominated solar minimum” versus “diffusion-dominated solar maximum”, instead they have dynamic behavior displaying variations in time and in timescales. This study also demonstrates the value of using AI methods to investigate non-linear physical processes in Space Physics in the era of big data.

The solar wind influence on geospace can be described as the sum of a directly driven component, or dayside reconnection, and an unloading component, associated with the release of magnetic energy via nightside reconnection. The two processes are poorly correlated on short time scales, but exactly equal when averaged over long time windows. Because of this peculiar property, regression models of ionospheric electrodynamics that are based on solar wind data are time scale specific: Models derived from 1 min resolution data will be different from models derived from hourly, daily, or monthly data. We explain and quantify this effect on simple linear regression models of various geomagnetic indices. We also derive a time scale-dependent correction factor that can be used with the Average Magnetic field and Polar current System model. Finally, we show how absolute estimates of the nightside reconnection rate can be calculated from solar wind measurements and geomagnetic indices.