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Solar cycle variability induced by stochastic fluctuations of BMR properties and at different amounts of dynamo supercriticality

Published online by Cambridge University Press:  23 December 2024

Pawan Kumar*
Affiliation:
Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
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Abstract

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Understanding the irregular variation of the solar cycle is crucial due to its significant impact on global climates and the heliosphere. Since the polar magnetic field determines the amplitude of the next solar cycle, variations in the polar field can lead to fluctuations in the solar cycle. We have explored the variability of the solar cycle at different levels of dynamo supercriticality. We observe that the variability depends on the dynamo operation regime, with the near-critical regime exhibiting more variability than the supercritical regime. Furthermore, we have explored the effects of the irregular BMR properties (emergence rate, latitude, tilt, and flux) on the polar field and the solar cycle. We find that they all produce considerable variation in the solar cycle; however, the variation due to the tilt scatter is the largest.

Type
Contributed Paper
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

References

Biswas, A., Karak, B. B., Usoskin, I., & Weisshaar, E. 2023,b Long-Term Modulation of Solar Cycles. Space Sci. Rev., 219b(3), 19.CrossRefGoogle Scholar
Biswas, A., Karak, B. B., & Cameron, R. 2022, Toroidal Flux Loss due to Flux Emergence Explains why Solar Cycles Rise Differently but Decay in a Similar Way. Phys. Rev. Lett., 129(24), 241102.CrossRefGoogle ScholarPubMed
Biswas, A., Karak, B. B., & Kumar, P. 2023, Exploring the reliability of polar field rise rate as a precursor for an early prediction of solar cycle. MNRAS, 526(3), 39944003.CrossRefGoogle Scholar
Charbonneau, P. 2020, Dynamo models of the solar cycle. Living Reviews in Solar Physics, 17(1), 4.CrossRefGoogle Scholar
Cameron, R. H. & Schüssler, M. 2017, Understanding Solar Cycle Variability. ApJ, 843(2), 111.CrossRefGoogle Scholar
Choudhuri, A. R., Chatterjee, P., & Jiang, J. 2007, Predicting Solar Cycle 24 With a Solar Dynamo Model. Physical Review Letters, 98(13), 131103.CrossRefGoogle ScholarPubMed
Gurgenashvili, E. and 8 colleagues 2016. Rieger-type Periodicity during Solar Cycles 14-24: Estimation of Dynamo Magnetic Field Strength in the Solar Interior. The Astrophysical Journal 826. doi: 10.3847/0004-637X/826/1/55 CrossRefGoogle Scholar
Golubeva, E. M., Biswas, A., Khlystova, A. I., Kumar, P., & Karak, B. B. 2023, Probing the variations in the timing of the Sun’s polar magnetic field reversals through observations and surface flux transport simulations. MNRAS, 525(2), 17581768.CrossRefGoogle Scholar
Jha, B. K., Karak, B. B., Mandal, S., & Banerjee, D. 2020, Magnetic Field Dependence of Bipolar Magnetic Region Tilts on the Sun: Indication of Tilt Quenching. ApJ Letters, 889(1), L19.CrossRefGoogle Scholar
Jouve, L., Proctor, M. R. E., Lesur, G. 2010. Buoyancy-induced time delays in Babcock–Leighton flux-transport dynamo models. Astronomy and Astrophysics 519. doi: 10.1051/0004-6361/201014455 CrossRefGoogle Scholar
Jiang, J., Wang, J.-X., Jiao, Q.-R., Cao, J.-B. 2018. Predictability of the Solar Cycle Over One Cycle. The Astrophysical Journal 863. doi: 10.3847/1538-4357/aad197 CrossRefGoogle Scholar
Karak, B. B. 2020, Dynamo Saturation through the Latitudinal Variation of Bipolar Magnetic Regions in the Sun. ApJ Letters, 901(2), L35.CrossRefGoogle Scholar
Karak, B. B. 2023, Models for the long-term variations of solar activity. Living Reviews in Solar Physics, 20(1), 3.CrossRefGoogle Scholar
Karak, B. B., Jiang, J., Miesch, M. S., Charbonneau, P., & Choudhuri, A. R. 2014,a Flux Transport Dynamos: From Kinematics to Dynamics. Space Sci. Rev., 186a, 561602.CrossRefGoogle Scholar
Karak, B. B., Mandal, S., & Banerjee, D. 2018, Double Peaks of the Solar Cycle: An Explanation from a Dynamo Model. ApJ, 866(1), 17.CrossRefGoogle Scholar
Karak, B. B., Kitchatinov, L. L., & Brandenburg, A. 2015, Hysteresis between Distinct Modes of Turbulent Dynamos. ApJ, 803, 95.CrossRefGoogle Scholar
Karak, B. B. & Miesch, M. 2017, Solar Cycle Variability Induced by Tilt Angle Scatter in a Babcock–Leighton Solar Dynamo Model. ApJ, 847, 69.CrossRefGoogle Scholar
Karak, B. B. & Miesch, M. 2018, Recovery from Maunder-like Grand Minima in a Babcock–Leighton Solar Dynamo Model. ApJ Letters, 860, L26.CrossRefGoogle Scholar
Kitchatinov, L. L. & Olemskoy, S. V. 2011, Does the Babcock–Leighton mechanism operate on the Sun? Astronomy Letters, 37, 656658.CrossRefGoogle Scholar
Kumar, P., Karak, B. B., & Vashishth, V. 2021,a Supercriticality of the Dynamo Limits the Memory of the Polar Field to One Cycle. ApJ, 913a(1), 65.CrossRefGoogle Scholar
Kumar, P., Nagy, M., Lemerle, A., Karak, B. B., & Petrovay, K. 2021,b The Polar Precursor Method for Solar Cycle Prediction: Comparison of Predictors and Their Temporal Range. ApJ, 909b(1), 87.CrossRefGoogle Scholar
Kumar, P., Biswas, A., & Karak, B. B. 2022, Physical link of the polar field buildup with the Waldmeier effect broadens the scope of early solar cycle prediction: Cycle 25 is likely to be slightly stronger than Cycle 24. MNRAS, 513(1), L112L116.CrossRefGoogle Scholar
Kumar, P., Karak, B. B., & Sreedevi, A. 2023,. MNRAS, submitted.Google Scholar
Mandal, S., Karak, B. B., & Banerjee, D. 2017, Latitude Distribution of Sunspots: Analysis Using Sunspot Data and a Dynamo Model. ApJ, 851, 70.CrossRefGoogle Scholar
Mordvinov, A. V., Karak, B. B., Banerjee, D., Chatterjee, S., Golubeva, E. M., Khlystova, A. I. 2020. Long-term Evolution of the Sun’s Magnetic Field during Cycles 15-19 Based on Their Proxies from Kodaikanal Solar Observatory. The Astrophysical Journal 902. doi: 10.3847/2041-8213/abba80 CrossRefGoogle Scholar
Mordvinov, A. V., Karak, B. B., Banerjee, D., Golubeva, E. M., Khlystova, A. I., Zhukova, A. V.& Kumar, P. 2022, Evolution of the Sun’s activity and the poleward transport of remnant magnetic flux in Cycles 21-24. MNRAS, 510, 1.Google Scholar
Nagy, M., Lemerle, A., Labonville, F., Petrovay, K., Charbonneau, P. 2017. The Effect of “Rogue” Active Regions on the Solar Cycle. Solar Physics 292. doi: 10.1007/s11207-017-1194-0 CrossRefGoogle Scholar
Olemskoy, S. V. & Kitchatinov, L. L. 2013, Grand Minima and North-South Asymmetry of Solar Activity. ApJ, 777, 71.CrossRefGoogle Scholar
Petrovay, K. 2020, Solar cycle prediction. Living Reviews in Solar Physics, 17(1), 2.CrossRefGoogle Scholar
Rieger, E., Share, G. H., Forrest, D. J., Kanbach, G., Reppin, C., Chupp, E. L. 1984. A 154-day periodicity in the occurrence of hard solar flares?. Nature 312, 623625. doi: 10.1038/312623a0 CrossRefGoogle Scholar
Stenflo, J. O. & Kosovichev, A. G. 2012, Bipolar Magnetic Regions on the Sun: Global Analysis of the SOHO/MDI Data Set. ApJ, 745(2), 129.CrossRefGoogle Scholar
Schatten, K. H., Scherrer, P. H., Svalgaard, L., Wilcox, J. M. 1978. Using Dynamo Theory to predict the sunspot number during Solar Cycle 21. Geophysical Research Letters 5, 411414. doi: 10.1029/GL005i005p00411 CrossRefGoogle Scholar
Sreedevi, A., Jha, B. K., Karak, B. B., & Banerjee, D. 2023, AutoTAB: Automatic Tracking Algorithm for Bipolar Magnetic Regions. ApJ Supplement, 268(2), 58.CrossRefGoogle Scholar
Vashishth, V., Karak, B. B., & Kitchatinov, L. 2021, Subcritical dynamo and hysteresis in a Babcock–Leighton type kinematic dynamo model. Research in Astronomy and Astrophysics, 21(10), 266.CrossRefGoogle Scholar
Vashishth, V., Karak, B. B., & Kitchatinov, L. 2023, Dynamo modelling for cycle variability and occurrence of grand minima in Sun-like stars: rotation rate dependence. MNRAS, 522(2), 26012610.CrossRefGoogle Scholar