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A solar cycle 25 prediction based on 4D-var data assimilation approach

Published online by Cambridge University Press:  24 September 2020

Allan Sacha Brun Open the ORCID record for Allan Sacha Brun [Opens in a new window] ,
Ching Pui Hung ,
Alexandre Fournier ,
Laurène Jouve ,
Olivier Talagrand ,
Antoine Strugarek  and
Soumitra Hazra
Allan Sacha Brun
Affiliation:
DAp/AIM, CEA Paris-Saclay, 91191 Gif-sur-Yvette, France
Ching Pui Hung
Affiliation:
DAp/AIM, CEA Paris-Saclay, 91191 Gif-sur-Yvette, France IPGP, Université de Paris, UMR 7154 CNRS, F-75005 Paris, France
Alexandre Fournier
Affiliation:
IPGP, Université de Paris, UMR 7154 CNRS, F-75005 Paris, France
Laurène Jouve
Affiliation:
Université de Toulouse, UPS-OMP, IRAP, 31028 Toulouse Cedex 4, France
Olivier Talagrand
Affiliation:
LMD, UMR 8539, Ecole Normale Supérieure, Paris Cedex 05, France
Antoine Strugarek
Affiliation:
DAp/AIM, CEA Paris-Saclay, 91191 Gif-sur-Yvette, France
Soumitra Hazra
Affiliation:
DAp/AIM, CEA Paris-Saclay, 91191 Gif-sur-Yvette, France
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Abstract

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Based on our modern 4D-var data assimilation pipeline Solar Predict we present in this short proceeding paper our prediction for the next solar cycle 25. As requested by the Solar Cycle 25 panel call issued on January 2019 by NOAA/SWPC and NASA, we predict the timing of next minimum and maximum as well as their amplitude. Our results are the following: the minimum should have occured within the first semester of year 2019. The maximum should occur in year 2024.4 ± 6 months, with a value of the sunspot number equal to 92±10. This is in agreement with the NOAA/NASA consensus published in April 2019. Note that our prediction errors are based on 1-σ measure and do not consider all the systematics, so they are likely underestimated. We will update our prediction and error analysis regularly as more data becomes available and we improve our prediction pipeline.

Keywords

Sundynamo11-yr cyclesolar cycle predictiondata analysisdata assimilation
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S354: Solar and Stellar Magnetic Fields: Origins and Manifestations , June 2019 , pp. 138 - 146
Copyright
© International Astronomical Union 2020

References

Brun, A. S. 2007. Towards using modern data assimilation and weather forecasting methods in solar physics. Astronomische Nachrichten, 328, 329CrossRefGoogle Scholar
Brun, A. S. & Browning, M. K. 2017, Magnetism, Dynamo Action and the Solar-Stellar Connection, Living Reviews in Solar Physics, 14, 4CrossRefGoogle ScholarPubMed
Cameron, R. & Schüssler, M. 2008 A Robust Correlation between Growth Rate and Amplitude of Solar Cycles: Consequences for Prediction Methods. The Astrophysical Journal, 685, 1291CrossRefGoogle Scholar
Clette, F. & Lefèvre, L. 2012, Are the sunspots really vanishing?. Anomalies in solar cycle 23 and implications for long-term models and proxies. Journal of Space Weather and Space Climate, 2, A06CrossRefGoogle Scholar
Clette, F. & Lefèvre, L. 2018, The new Sunspot Number: continuing upgrades and possible impacts, IAU Symposium, 17CrossRefGoogle Scholar
Hathaway, D. H. 2015. The Solar Cycle. Living Reviews in Solar Physics, 12, 4CrossRefGoogle ScholarPubMed
Hung, C. P., Jouve, L., Brun, A. S., Fournier, A., Talagrand, O., et al. 2015. Estimating the Deep Solar Meridional Circulation Using Magnetic Observations and a Dynamo Model: A Variational Approach. The Astrophysical Journal, 814, 151CrossRefGoogle Scholar
Hung, C. P., Brun, A. S., Fournier, A., Jouve, L., Talagrand, O., Zakari, M., et al. 2017. Variational Estimation of the Large-scale Time-dependent Meridional Circulation in the Sun: Proofs of Concept with a Solar Mean Field Dynamo Model. The Astrophysical Journal, 849, 160CrossRefGoogle Scholar
Jouve, L. & Brun, A. S. 2007. On the role of meridional flows in flux transport dynamo models. Astronomy and Astrophysics, 474, 239–250CrossRefGoogle Scholar
Jouve, L., Brun, A. S., Talagrand, O., et al. 2011. Assimilating Data into an α Ω Dynamo Model of the Sun: A Variational Approach. The Astrophysical Journal, 735, 31CrossRefGoogle Scholar
Karak, B. B. & Choudhuri, A. R. 2011. The Waldmeier effect and the flux transport solar dynamo. Monthly Notices of the Royal Astronomical Society, 410, 1503–1512Google Scholar
Petrovay, K. 2019, Solar Cycle prediction, arXiv e-prints, https://arxiv.org/abs/1907.02107Google Scholar
Sanchez, S., Fournier, A., & Aubert, J. 2014, The Predictability of Advection-dominated Flux-transport Solar Dynamo Models. The Astrophysical Journal, 781, 8CrossRefGoogle Scholar
Talagrand, O. 2010, in Data Assimilation: Making Sense Of Observations, ed. Lahoz, W., Khattatov, B. & Menard, R. (Berlin: Springer)Google Scholar
Weber, M., Upton, L. Biesecker, D., et al. 2019, Solar Cycle 25 Prediction, https://www.swpc.noaa.gov/news/solar-cycle-25-forecast-updateGoogle Scholar