The modulation of anomalous and galactic cosmic ray oxygen over successive solar cycle minima
R.D. Strauss, R.A. Leske, J.S. Rankin

TL;DR
This study investigates the differing behaviors of anomalous and galactic cosmic ray oxygen during recent quiet solar minima by simulating their acceleration and transport, revealing less turbulence leads to higher GCR and lower ACR fluxes.
Contribution
The paper introduces a simulation model constrained by recent observations to explain the ACR/GCR discrepancy during recent solar minima, highlighting the role of turbulence in cosmic ray modulation.
Findings
Less turbulent conditions increase GCR fluxes.
Lower ACR fluxes are due to less efficient acceleration.
The model explains the ACR/GCR discrepancy in 2009 and 2020.
Abstract
Both the recent 2009 and 2020 solar minima were classified as unusually quiet and characterized with unusually high galactic cosmic ray (GCR) levels. However, unlike the trends from previous decades in which anomalous cosmic ray (ACR) and GCR levels strongly agreed, the ACR intensities did not reach such high record-setting levels.This discrepancy between the behaviour of GCRs and ACRs is investigated in this work by simulating the acceleration and transport of GCR and ACR oxygen under different transport conditions. After using recent observations to constrain any remaining free parameters present in the model, we show that less turbulent conditions are characterized by higher GCR fluxes and low ACR fluxes due to less efficient ACR acceleration at the solar wind termination shock. We offer this as an explanation for the ACR/GCR discrepancy observed during 2009 and 2020, when compared…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSolar and Space Plasma Dynamics · Atmospheric Ozone and Climate · Climate variability and models
