S1: Long-term Solar Variability (solar cycle and beyond)
Moderator:
Alexei Pevtsov (National Solar Observatory, USA), apevtsov@nso.edu
Co-Moderators:
Dibyendu Nandi (CESSI/IISER Kolkata, India), dnandi AT iiserkol.ac.in
Claudio Corti (NASA/NSF Community Coordinated Modeling Center (CCMC), USA), corti@hawaii.edu
Advisor:
Ilya Usoskin (University of Oulu, Finland), ilya.usoskin@oulu.fi
Impact:
Driver of long term space environment variability in heliosphere, geospace and other planets.
Climate.
Introduction:
The Sun’s dynamic activity is manifested across multiple timescales that are relevant for forcing of the near-Earth space environment and planetary atmospheres and climate. The solar magnetic cycle and its variability modulate magnetic, radiative and particulate fluxes over long-term and determine the frequency of space weather events such as flares and CMEs. Knowledge of past solar variability observed and recorded through various parameters, including historical space weather events inform us about the range of possible solar fluctuations.
Objectives:
The goals of this cluster are to reconstruct and constrain past solar activity, extreme space weather episodes (if possible, their impacts), help assess predictive models of solar activity ranging from solar dynamo models, solar surface flux transport models to coronal and heliospheric field evolution models and transition validated data driven computational models to operational space weather (and climate) forecasting tools.
Essential Space Environment Quantities (ESEQ) / Forecasting Goals:
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- Historical sunspot number time series (reconstructed and observed)
- Solar spectral irradiance time series (reconstructed and observed)
- Solar open flux time series
- Historical solar wind time series (reconstructed)
- Predicted magnetic flux and sunspot number time series
- Predicted solar irradiance time series
- Extreme solar events
Action topics:
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- Solar activity indices for constraining long-term variability
- Extreme space weather events and their impacts
- Assessing predictive models of solar activity.