Observational Insights of Solar-Terrestrial Coupling from the Ionospheric and Surface Impacts during Solar Perturbations

Abstract

We investigated three intense solar–terrestrial disturbance chains in 2022 viz. an X1.5?flare on ?February 15, an X1.0?+?M8.7?flare based CME sequence on ?October 2 and a G3-geomagnetic storm driven by multiple CMEs on October 4?thereby aiming to diagnose how impulsive solar energy input cascades through the coupled ionosphere and troposphere system. Multi platform data sets were used: GOES/SDO EUV–X ray fluxes, ESA Solar Orbiter imagery, SWPC solar wind and IMF parameters, ground based ionosondes (TEC,?NmF2,?Ne,?f0F2) and surface meteorological records. Results reveal a spectrum of responses governed by event type and magnetic field geometry. Immediate ionospheric impacts show a TEC bulge within?

Introduction

Solar flares are sudden energy bursts from the Sun’s surface, often associated with Coronal Mass Ejections (CMEs)—large expulsions of plasma and magnetic fields into space. These events can disturb Earth’s magnetosphere, ionosphere, and atmospheric layers, causing effects such as radio blackouts, geomagnetic storms, and auroras.

Key Events and Observations:

1. February 15, 2022 – X-Class Flare & CME

• Captured by: ESA’s Solar Orbiter (EUI/FSI) and SOHO’s LASCO C2/C3.

• Event: Longest solar prominence observed (~3 million km long).

• Sunspot AR2941 produced an M1.3-class flare, causing a minor radio blackout over South America.

• CME arrival: ~17.5 hours after the flare, leading to a Kp=6 geomagnetic storm.

• Significance: Major visual evidence of Earth-directed solar activity.

2. October 2, 2022 – X1.0-Class Flare

• Captured by: NASA’s Solar Dynamics Observatory (SDO).

• Origin: Sunspot AR3110.

• Effect: Caused shortwave radio blackouts in the Pacific and parts of North America.

• Rapid ionospheric reaction (<5 minutes) and tropospheric pressure anomalies observed.

3. October 4, 2022 – G2-G3 Geomagnetic Storm

• Trigger: CME from Sept 30 flare (AR13141 complex).

• Sunspot AR3112 became Earth-facing and highly active.

• Effect:

  • Sustained Kp=7 storm conditions.

  • Polar cap absorption (PCA).

  • 20+ TECu ionospheric total electron content spikes.

  • Surface pressure anomalies up to 3 hPa at high latitudes.

• Rare Phenomenon: A "Cannibal CME" occurred—fast CMEs overtook slower ones, enhancing impact.

• Auroras Observed:

  • Pink auroras: Resulting from deep atmospheric penetration of solar particles (<100 km).

  • Pulsating auroras: Possibly linked to “chorus waves” in Earth’s magnetosphere.

Scientific and Instrumental Insights:

• Instruments Used:

  • SOHO LASCO C2 & C3: White-light coronagraphs for CME tracking.

  • SDO AIA 304, 131, 171: EUV imaging to study flare and plasma dynamics.

  • EUI/FSI (Solar Orbiter): Wide-field EUV imaging of solar disc and corona.

• Ionospheric & Atmospheric Response:

  • F2 Layer of ionosphere showed most agitation.

  • Surface-level changes included temperature, pressure, and electric potential variations.

  • X-rays and EUV bursts influenced atmospheric electric circuits and surface pressure.

  • SFE (Solar Flare Effects) identified through crochet-like magnetic signatures.

Conclusion

This study demonstrates the profound impact of flares driven associated space weather events on ionosphere and surface weather systems. The comparative analysis of February 15, October 2, and October 4, 2022, reveals distinct solar–terrestrial interaction patterns. Although October 2 experienced minimal geomagnetic disturbance, the Earth-facing X1.0 flare triggered the strongest TEC and F-layer enhancements, with evident ionospheric–surface coupling. The October 4 geomagnetic storm exhibited the highest variability and nonlinear behaviour, poorly captured by Kp alone, and showed the widest Ne–NmF2 gap. In contrast, February 15\'s far-side CME produced limited ionospheric effects despite strong flare activity. Surface variations viz. temperature dips and humidity shifts mirrored ionospheric changes. Future work should develop real-time coupling indices and advanced models to improve predictive capabilities.

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Copyright

Copyright © 2025 Abhijit Banerjee, Rina Bhattacharya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.