1. Secular variation in Germany from repeat station data and a recent global field model Monika Korte and Vincent Lesur Helmholtz Centre Potsdam, German Research Centre for Geosciences - GFZ Outline: Motivation German repeat station data The global field model GRIMM-2 Comparison of secular variation of model and data Conclusions

2. Motivation Since 1999 satellites like Ørsted and CHAMP provide a dense global geomagnetic data coverage. These, together with geomagnetic observatory data, lead to increa- singly accurate global field models with good secular variation descriptions. Do repeat station data from areas with relatively good observatory coverage provide additional useful signal?

3. German repeat station data - Overview Repeat station measurements in Germany started in 1999/2000 Improved data processing with local/regional variometer 12 variometer stations used in nearly each survey 3 geomagnetic observatories (WNG, NGK, FUR – BFO only established in 2005)

4. Why local variometers? Data processing to obtain internal field annual means for repeat stations: C(x i,t mean ) = C(x i,t i ) – C(O,t i ) + C(O, t mean ) Repeat station “annual mean” of component C Observatory annual mean of component C Repeat station measurement value at time t i Observatory recording at time t i Assumptions: - Secular variation is the same - External variations are the same - Induced variations are the same at repeat station and observatory This difference can be determined more robustly from (quiet) night time differences with a local variometer

5. German repeat station data - Details Surveys in 1999/2000 (half network per year) 2001/2002 (half network per year) 2003 (about 75% of full network) 2004 (full network) 2006 (full network) 2008 (full network) All data reduced to annual mean centered on the middle of the year the measurements were done.

6. Global field model GRIMM2 Continuous model valid for 2001 – 2008

7. Global field model GRIMM2 - Data CHAMP satellite data - X and Y in solar-magnetic (SM) coordinates between +/-55° magnetic latitude - geocentric X,Y,Z at high latitudes - selected for quality: acceptabel quality flags, corrected for orientation errors - selected for magnetically quiet data: IMF Z-componente positive, Vector Magnetic Disturbance (Thomson & Lesur, 2007) < 20 nT and norm of its derivative < 100 nT/day - low/mid latitude data addionally selected by local time: LT between 23:00 and 5:00, sun below horizon Observatory data - hourly means in same coordinate systems and with same selection criteria applied

8. Global field model GRIMM2 - Modelling Core field and secular variation - spherical harmonics with time dependence by 5 th order B-splines up to SH degree 16, knot-point spacing 400days - weak regularization by minimizing squared second time derivative of radial field at the CMB (high degree core field SH degrees influenced) - additional regularization to mitigate effect of additional degrees of freedom introduced by 5 th order B-splines: minimizing squared third time derivative of radial field at Earth’s surface (low SH degrees influenced) Toroidal magnetic field modelled to take into account field aligned currents over polar regions (constant term with annual variation) Ionospheric field over polar regions modelled by assumption of temporally varying currents in a thin shell (110 km above Earth)

9. Comparison of global model and annual means data “Annual means” of GRIMM2 obtained as average of 10 core field values per year Model values subtracted from repeat station and observatory annual means Annual mean data are not free from external field variations! (Example: annual means of European observatories ordered by geomag. Latitude with CM4 model core field subtracted)

10. Empirical external field correction WNG NGK FUR 1. Core field model removed: - lithospheric offset - external field influences 2. Constant offsets removed: - homogeneous residual pattern Black lines: average resisual pattern 3. Average residual pattern removed from data (black lines)

11. Field change from 2000.5 to 2008.5 Rather linear change X: ca. 70 nT or 9nT/yr Y: ca. 300 nT or 38 nT/yr Z: ca. 250 nT or 31 nT/yr NGK annual means

12. Locations of repeat stations with local variometer North component X East component Y Vertical component Z Vector anomaly maps R-SCHA model by Korte & Thébault, 2007

13. Residuals of repeat stations with local variometer

14. Scatter or systematic trends? Measurement uncertainties from scatter among measurements at one location in the order of D (Y) 1.6 nT H (X) 1 nT Z 0.7 nT Linear regression of variometer station time series: - trend up to +/- 1.1 nT/yr occur in all components - trends mostly in the order of 0 to 0.5 nT/yr in all components - often low correlation Problem: global model not reliable for 2000.5 and 2008.5 (ends), linear regression of data between 2001.5 and 2006.5 only (3 to 4 epochs per time series only, no proper statistics!): - trends in the same order in general, but hardly similar at the same stations - low correlation for about half of the time series

15. Scatter or systematic trends? Results where similar linear trends exist and correlation is [high: - tel X (0.53 nT/yr), Y (-0.51 nT/yr), Z (-0.56 nT/yr) - eil Y (-0.99 nT/yr) - [kar X (-0.53 to -0.63 nT/yr)] These values are rather high, but not completely unreasonable compared to theory: Thébault et al. (2009) investigated the expected induced signal based on the vertically integrated susceptibility (VIS) model by Hemant and Maus (2005). Their results are: * 0.1 nT/yr for western Europe * up to 0.3 – 0.6 nT/yr for eastern Europe * maximal globally (very few regions) 0.65 – 1.3 nT/yr

16. Tentative interpolation of linear trends in residuals North component X East component Y Vertical component Z

17. Anomalies and linear trend - X North component X

18. Anomalies and linear trend - Y East component Y

19. Anomalies and linear trend - Z Vertical component Z

20. Residuals of further repeat stations Scatter of up to 10 nT in time series at many locations without variometer

21. Conclusions Regional secular variation is described well by recent global field models based on satellite and observatory data. High accuracy repeat station data might provide information about induced sources of crustal field, but it is very difficult to discriminate between signal and noise Longer time series are needed for more reliable statistics