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Moderator: Alexei Pevtsov (National Solar Observatory, USA)

Co-Moderators: Dibyendu Nandi (CESSI/IISER Kolkata, India)

Claudio Corti (GSFC CCMC, USA)

Advisor:

Ilya Usoskin (University of Oulu, Finland)

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.

Action Teams:

S1-01: Long-term solar variability (Lead: Ilya Usoskin)
S1-02: Worst-case scenario for extreme solar events (Lead: Ilya Usoskin)
S1-03: Data sets of historical observations of solar and geomagnetic activity (Lead: Alexander Pevtsov)
S1-05: Decadal-scale Solar Activity Prediction Challenge (Lead: Claudio Corti & Dibyendu Nandi)
S1-06: Long-term GCR modeling during Grand Minima (Lead: Claudio Corti)

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Yeates et al (2024)

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Recent activities and plans

Several conferences/group meeting (SC9, sunspot number)
Inter-Division B-E WG Coordination of Synoptic Observations of the Sun (Chair: Sabrina Bechet, Belgium, Co-chair: Dipankar Benerjee, India)

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S1-03 Data sets of historical observations of solar and geomagnetic activity

Overview

Over the past centuries, the solar and geoscience communities have accumulated a significant heritage of historical observations pertaining to past solar and geomagnetic activity. Those records contain irreplaceable information for studies of long-term changes in the sun and geomagnetic field. Unfortunately, most of those data are poorly organized and rarely accessible for modern scientific research.

Current efforts

Continued creation of a catalog of historical data from around the world
Creation of a website to provide easy access
Develop plans to process data needed for specific community driven projects

Data processing for Sunspot number version 3

Future efforts

Development of citizen science platform to help process historical records

'Sun Spot'ter: A citizen science environment for the extraction of Sunspot data from Sunspot drawings

Create a classification system to help identify the datasets at highest risk of loss and flag them for priority preservation

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New team, S1-05:�Decadal-scale Solar Activity Prediction Challenge

Leads: Claudio Corti (CCMC), Dibyendu Nandi (CESSI/IISER)

From the S1 cluster review for COSPAR space weather roadmap:

“The task of predicting and forecasting the solar cycle is a major challenge in the field of heliophysics. Significant progress has been achieved in the last decade in developing such predictive capabilities. However, there is significant divergence in forecasts using multiple different methodologies, such as statistical correlations based on precursors of solar activity, time-series analysis of solar cycle observations, machine learning techniques, solar surface flux transport (SFT) models, solar dynamo models, etc.”

Solar-cycle prediction challenge, similar to other efforts (SEPVAL, CME arrival, solar wind, etc):

Use past SC to produce predictions for current SC, not just SSN, but also open flux, F10.7, GCRs, etc
Develop solar-cycle scoreboard to compare predictions with observations and provide validation to predicting models
Long-term plan: keep doing it for next solar cycles

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New team, S1-06:�Long-term GCR modeling during Grand Minima

Lead: Claudio Corti (CCMC)

Why: understanding time variations of cosmogenic isotopes during Dalton and Maunder minima
What do we need?

Global heliosphere model: solar wind, IMF, current sheet
Transport parameters: turbulence, diffusion, and drift theories

Two approaches to GCR modeling:

Bottom-Up (ab-initio models): from turbulence transport models to GCR observations
Top-Down (empirical models): from GCR observations to diffusion coefficients

Goals:

Systematic comparison of different modeling approaches
Reach consensus on which ingredients and assumptions are needed to properly interpret GCR data
Full-stack GCR modeling: solar dynamo → surface flux transport → open magnetic flux and solar wind → GCR transport

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