Understanding mechanisms underlying solar cycle predictability with a general framework

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Table 1.

Summary of key parameters, governing integration terms, and predictive skills of selected solar dynamo models.

	Model 1	Model 2	Model 3	Model 4	Model 5
Parameters:
Ω	Eq. (4) of DC99	Eqs. (20-21) of PC20	Eqs. (20-21) of PC20	Eq. (10-11) of ZJ22	Eq. (10-11) of ZJ22
U_p	Eq. (5) of DC99	Eq. (5) of DC99	/	Eq. (12) of ZJ22	/
η	Eq. (6) of DC99	Eq. (22) of PC20	Eq. (22) of PC20	Eq. (14) of ZJ22	Eq. (14) of ZJ22
S	Eq. (7) of DC99	Eq. (5-8) of CB11	Eq. (5-9) of CB11	Eq. (6-9) of ZJ22	Eq. (6-9) of ZJ22
γ	/	/	/	Eq. (13) of ZJ22	Eq. (13) of ZJ22
Integration term dominating $\frac{d Φ^{N}}{dt}$ $Mathematical equation: $ \frac{d\Phi^N}{dt} $$	I₁	I₁	I₂ + I₃	I₁	I₁
Exhibits predictive skill	Yes	Yes	No	Yes	Yes
Physical mechanism for the predictive skill	The surface poloidal field is efficiently coupled back into the dynamo loop	The surface poloidal field is efficiently coupled back into the dynamo loop	/	The surface magnetic field represents the radial component of the interior poloidal field	The surface magnetic field represents the radial component of the interior poloidal field

Notes. DC99: Dikpati & Charbonneau (1999); PC20: Charbonneau (2020); CB11: Charbonneau & Barlet (2011); ZJ22: Zhang & Jiang (2022).

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