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Table 3 Derived SV candidate model

From: A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation

\(l\) \(m\) \(\dot{g}_{l}^{m}\) \(\dot{h}_{l}^{m}\) \(\delta \dot{g}_{l}^{m}\) \(\delta \dot{h}_{l}^{m}\)
1 0 \(1.88\) \(0.21\)
1 1 \(3.82\) \(- 24.64\) \(0.27\) \(0.26\)
2 0 \(- 10.85\) \(0.21\)
2 1 \(- 6.21\) \(- 26.05\) \(0.24\) \(0.21\)
2 2 \(- 4.59\) \(- 17.18\) \(0.18\) \(0.18\)
3 0 \(0.31\) - \(0.19\) -
3 1 \(- 4.94\) \(4.51\) \(0.15\) \(0.15\)
3 2 \(2.03\) \(- 0.58\) \(0.15\) \(0.15\)
3 3 \(- 11.74\) \(- 0.45\) \(0.12\) \(0.12\)
4 0 \(- 0.04\) \(0.11\)
4 1 \(- 0.37\) \(- 0.10\) \(0.11\) \(0.10\)
4 2 \(- 5.91\) \(4.49\) \(0.10\) \(0.10\)
4 3 \(2.61\) \(4.14\) \(0.09\) \(0.09\)
4 4 \(- 5.05\) \(- 4.62\) \(0.08\) \(0.08\)
5 0 \(- 0.95\) \(0.07\)
5 1 \(0.00\) \(- 1.40\) \(0.07\) \(0.07\)
5 2 \(0.21\) \(2.89\) \(0.07\) \(0.07\)
5 3 \(1.16\) \(0.69\) \(0.06\) \(0.06\)
5 4 \(0.73\) \(1.49\) \(0.06\) \(0.06\)
5 5 \(0.95\) \(- 0.08\) \(0.05\) \(0.05\)
6 0 \(- 0.65\) \(0.05\)
6 1 \(- 0.52\) \(- 0.25\) \(0.05\) \(0.04\)
6 2 \(0.62\) \(- 1.37\) \(0.04\) \(0.04\)
6 3 \(1.48\) \(- 1.27\) \(0.04\) \(0.04\)
6 4 \(- 1.14\) \(0.78\) \(0.03\) \(0.03\)
6 5 \(0.33\) \(0.03\) \(0.04\) \(0.04\)
6 6 \(0.92\) \(0.85\) \(0.03\) \(0.03\)
7 0 \(- 0.53\) \(0.03\)
7 1 \(- 0.18\) \(0.32\) \(0.03\) \(0.02\)
7 2 \(0.05\) \(0.96\) \(0.03\) \(0.03\)
7 3 \(0.90\) \(- 0.05\) \(0.02\) \(0.02\)
7 4 \(- 0.09\) \(- 0.01\) \(0.03\) \(0.02\)
7 5 \(- 0.70\) \(- 1.27\) \(0.02\) \(0.02\)
7 6 \(- 0.74\) \(- 0.03\) \(0.02\) \(0.02\)
7 7 \(0.16\) \(0.12\) \(0.02\) \(0.02\)
8 0 \(- 0.10\) \(0.02\)
8 1 \(0.05\) \(- 0.62\) \(0.02\) \(0.02\)
8 2 \(- 0.07\) \(0.64\) \(0.02\) \(0.01\)
8 3 \(0.32\) \(- 0.06\) \(0.02\) \(0.02\)
8 4 \(- 0.02\) \(0.64\) \(0.01\) \(0.01\)
8 5 \(0.20\) \(- 0.11\) \(0.01\) \(0.01\)
8 6 \(0.15\) \(- 0.50\) \(0.01\) \(0.01\)
8 7 \(- 0.15\) \(0.27\) \(0.01\) \(0.01\)
8 8 \(0.23\) \(0.02\) \(0.01\) \(0.01\)