On the possibility of extending the IGRF predictive secular variation model to a higher SH degree
 383 Downloads
 4 Citations
Abstract
The International Geomagnetic Reference Field (IGRF) is an internationally agreed global spherical harmonic model of the Earth’s magnetic field of internal origin. It is currently computed every five years in the form of a model describing this field up to degree 13 at a reference epoch, plus a secular variation model up to degree 8, best estimating the linear evolution of this field over the following five years. Such a simple description of the field evolution is thought to provide a good enough prediction of the field, both for navigational and internationally agreed reference purposes (the very purpose of IGRF models). In particular, it assumes that any change in the field described by spherical harmonic degrees between 9 and 13 may be neglected over five years, given the uncertainties already involved in the determination of all other coefficients, and the practical accuracy needed for most IGRF applications. Recent progress in global field modelling based on increasingly accurate and numerous satellite data however show that all field coefficients can now be computed with much higher accuracy than possible in the past, and that higher degree secular variation coefficients could therefore also be considered for inclusion in IGRF models. The present short note intends to investigate the potential benefit of extending the IGRF predictive secular variation model to degrees higher than 8, given our current knowledge of the way the field behaves over time periods of five years.
Key words
Geomagnetism field modelling reference field secular variation1. Introduction
The International Geomagnetic Reference Field (IGRF) is a reference mathematical model of the Earth’s main magnetic field, presented in terms of Gauss coefficients up to degree and order 13 and their predictive first time derivatives up to degree and order 8 for the upcoming five years. It is released by the International Association of Geomagnetism and Aeronomy (IAGA) and results from the collaboration effort between magnetic field modellers and the institutes that collect and make magnetic field data available. Its recent release, IGRF11 (Finlay et al., 2010) comprises retrospectivelyproduced models for previous epochs from 1900 to 2005 (definitive 1945–2005), an estimate of the main field in 2010 up to spherical harmonic (SH) degree and order 13 and a predictive secular variation up to SH degree and order 8 with predictive value until 2015. Thus, the predictive secular variation does not include the smallerscale evolution of the field between degrees 9 and 13.
By construction, the IGRF model is defined as a weighted average of candidate models submitted for evaluation by various teams (Finlay et al., 2010). There is no agreement on how to derive the optimum secular variation model and IGRF candidates generally follow rather different methodologies. Classically, the secular variation computed as the average of the main field time derivative over the previous five years is considered to approximate well the secular variation over the next five years (Beggan and Whaler, 2010). More instantaneous estimates may nevertheless be proposed. They are obtained from finite difference of data extrapolated at different future epochs (Chambodut et al., 2010), from timevarying models extrapolated to the epoch of the main field IGRF model (e.g., Maus et al., 2010; Olsen et al., 2010; Thébault et al., 2010) or to future epochs (Lesur et al., 2010). In this short note however, we focus on the interest of computing predictive secular variation models as the average rate of change of the main field over the previous five years as given by retrospective models.
Recent field models such as POMME3.0 (Maus et al., 2006), CHAOS (Olsen et al., 2006), xCHAOS (Olsen and Mandea, 2008), GRIMM (Lesur et al., 2008) or CHAOS2 (Olsen et al., 2009), consider an instantaneous internal field up to SH degree 13 or higher and a temporal expansion of the Gauss coefficients made in terms of some set of functions (degree 2 polynomials for POMME3.0, cubic Bsplines for CHAOS and xCHAOS, order 5 Bsplines for GRIMM and CHAOS2). As a result, these models also consider an instantaneous secular variation up to at least degree 13 and a second time derivative considered robust up to degree 5 or 6. The availability of these high quality retrospective field models provides the basis and stimulus for extending the limits of any operational model such as IGRF towards more spatially accurate shortterm predictions.
Here we thus consider the potential benefits of improving IGRF’s predictive power by increasing the SH degree of its predictive secular variation. We will show that these high degree coefficients correlate reasonably well over time, are quite well determined and have potential predictive value over the five years validity period of IGRF.
2. Correlation between Average Secular Variation Estimates from Published Models

SV1 the mean secular variation between 1980 and 1999, by taking the difference between the field at 1980 and 1999 from CM4;

SV2 the mean secular variation between 1994 and 1999, by taking the difference between the field at 1994 and 1999, again from CM4;

SV3 the mean secular variation between 1999 and 2004, still computed in the same way but from CHAOS2s;

SV4 the mean secular variation between 2004 and 2009, again computed from CHAOS2s;
This first calculation shows that a better correlation is achieved when considering an average secular variation computed over a five year time period (SV2) before the reference epoch, than when considering an average secular variation computed over a significantly longer time period (SV1). This is particularly true for the largest degrees which, as noted by one of the reviewers, can be related to the fact that higher degrees involve shorter timescales (e.g. Hulot and Le Mouël, 1994). To confirm this first result, we also computed the per degree correlation between SV3 (1999–2004) and SV4 (2004–2009), both computed from CHAOS2s, now considering 2004 as the reference epoch (Fig. 1). Again, and as expected, SV3 and SV4 appear to correlate very well.
3. Comparison between Main Field Forecasts
Having a 100% relative improvement then means that, by making use of the given secular variation, we properly predicted the field at the end of the epoch. By contrast, a relative improvement of 0% means that the secular variation was of no use.
This relative improvement, for the Gauss coefficients taken at the Earth’s surface, is plotted in Fig. 2 when using SV1, and in Fig. 3 when using SV2, where each line corresponds to a different value of c. It is interesting to see that even SV1 which, we recall, is the mean secular variation of CM4 between 1980 and 1999, produces some improvement in the prediction of all degrees, including those above 8.
Much more interesting, however, is the result obtained when considering SV2 (Fig. 3), which now suggests a strong benefit of using this model up to degree 12. Degrees 9 and 10 are now particularly interesting as they show a relative improvement well above 60%, comparable to that for degrees up to 8 and in fact higher than for degrees 3 and 7. Again, this suggests that considering a predictive secular variation up to at least degrees 9 and 10 constitutes a real improvement when comparing to the present static field approach of IGRF. Infact, Fig. 3 again suggests that considering degrees 11 and 12 is also of some potential benefit, although the relative improvement in the misfit is clearly inferior.
Finally, note that, as we in fact also checked, carrying out a similar calculation with SV3, and using 2004 and 2009 from CHAOS2s as initial and final reference field models, does not bring any useful additional information, as SV3 is also directly inferred from CHAOS2s, leading the results to mainly reflect the temporal regularisation used in CHAOS2s, which we already discussed in the previous section.
4. Testing Predictions from a Degree 13 ExtendedIGRF10 Secular Variation Model against a Fully Independent Model
There are two main limitations to the tests considered so far. First, all tested secular variation models were derived from parent models in a way that is not necessarily representative of the way IGRF secular variation models are constructed. Second, all comparisons were made with control models (be it SV3, SV4 or CHAOS2s for epoch 2004), that are not fully independent from the tested secular variation models. In particular, because CHAOS2s was built from data covering the 1997–2009.5 time period, with some amount of temporal regularisation, it may rightly be objected that its estimate of the 2004 field may be favourably biased towards the prediction inferred from SV1 and SV2, both based on CM4, which considered data up to 2002.5. In order to complement the tests already reported, we therefore decided to perform a final set of tests that avoid those limitations.
Data used to construct POMME5post05 according to location. Mid latitudes refer to track segments covering −60° to 60° geomagnetic latitude (Mlat) and high latitudes refer to overlapping tracks at < −50° and > 50° (Mlat).
n. data used  

Scalar data (mid latitude)  358345  
Scalar data (north polar)  239928  
Scalar data (south polar)  235503  
Vector data (mid latitude)  252879 
These results again strongly suggest that the high degrees of the secular variation (at least for degrees 9 and 10), computed as a mean over the preceding 5 year period, were sufficiently well resolved and correlated with the true secular variation to already have been included in IGRF10.
5. Conclusion
The present study clearly suggests that considering degrees above 8 for inclusion in an IGRF type of predictive secular variation model could be of potential use, provided such a model is based on an estimate of the past secular variation over a short enough period of time, typically 5 years, as is usually the case for standard IGRF predictive secular variation models. This benefit is best expressed in relative terms per degree of the field it would predict (Fig. 7), in which case each degree up to degree 12 appears to bring as much benefit as degrees up to 8, already included in standard IGRF predictive secular variation models.
Our results also suggest that, at least over the time period 2005–2009 we considered, degrees 9 and 10 of such an extended IGRF predictive secular variation would correlate with secular variation models computed a posteriori from independent data, just as well as degrees 5 to 8 (Figs. 1 and 5).
From these results we conclude that at least degrees 9 and 10, if not also degrees 11 and 12, of an IGRF type of predictive secular variation model, would have as good a predictive power as the first eight degrees already included in standard IGRF predictive secular variation models. From this perspective, we thus see no objective reasons to restrict this model to degree 8 rather than degree 10 or even degree 12.
It is nevertheless important to recognise that the improvement brought by the inclusion of higher degrees in IGRF predictive secular variation models would mainly be in relative terms per degree of the field, and would not compensate for the absolute errors entailed by the uncertainties with which this secular variation can be constructed up to degree 8. The total power accounted for by degrees above 8 is indeed small when compared to the errors associated to the lower degrees (recall Fig. 4). But we also note that even in absolute terms, errors associated with neglecting degrees 9 and 10 in an IGRF predictive secular SV would still be larger than the error produced by the uncertainties associated with degrees 7 and 8 already included in such models (again, recall Fig. 4). This, we believe, again pleads in favour of at least including degrees 9 and 10 in the construction of forthcoming IGRF predictive secular variation models.
It is finally important to keep in mind that all of the results derived above rely on the availability of highquality data provided by ongoing satellite missions. Should such satellite data suddenly fail to be available for some significant fraction of the five years time between two successive IGRF models, the situation would clearly be different. From this perspective, keeping the maximum degree of IGRF predictive secular variation models to its present value would definitely avoid having to adjust this degree to the quality of the available data every time a new IGRF model is to be produced. This concern is a significant practical one, given the operational purpose of IGRF models. But it is not one based on scientific grounds. In fact, it is well worth noting that a similar issue would anyway also affect the maximum degree of the IGRF main field, currently set to degree 13, but which used to be set to degree 10 before continuous magnetic measurements from space became available. Since the continuous (or possibly nearcontinuous, in case of a slight delay) availability of such measurements, with improved quality, should be ensured with the soon to be launched Swarm mission (FriisChristensen et al., 2006, 2009), it is our opinion that increasing the maximum degree of the IGRF predictive secular variation model to take advantage of the benefit it would bring should seriously be considered, especially if the retrospective analysis of the next IGRF11 set of models confirms that the provision of nonzero secular variation coefficients would have resulted in an improved prediction all the way up to degree 12 or even 13.
Notes
Acknowledgments
The authors would like to thank the reviewers B. Langlais and I. Wardinski for their constructive reviews and the guest editor Chris Finlay for his most helpful comments. This work was partly funded by CNES. This is IPGP contribution number 3042.
References
 Beggan, C. and K. Whaler, Forecasting secular variation using core flows, Earth Planets Space, 62, this issue, 821–828, 2010.CrossRefGoogle Scholar
 Chambodut, A., B. Langlais, M. Menvielle, E. Thébault, A. Chulliat, and G. Hulot, Candidate models for the IGRF11th generation making use of extrapolated observatory data, Earth Planets Space, 62, this issue, 745–751, 2010.CrossRefGoogle Scholar
 Finlay, C. C., S. Maus, C. D. Beggan, M. Hamoudi, F. J. Lowes, N. Olsen, and E. Thébault, Evaluation of candidate geomagnetic field models for IGRF11, Earth Planets Space, 62, this issue, 787–804, 2010.CrossRefGoogle Scholar
 FriisChristensen, E., H. Lühr, and G. Hulot, SWARM: A constellation to study the Earth’s magnetic field, Earth Planets Space, 58(4), 351–358, 2006.CrossRefGoogle Scholar
 FriisChristensen, E., H. Lühr, G. Hulot, R. Haagmans, and M. Purucker, Geomagnetic research from space, EOS, 90(25), 213–214, 2009.CrossRefGoogle Scholar
 Hulot, G. and J.L. Le Mouël, A statistical approach to the Earth’s main magnetic field, Phys. Earth Planet. Inter., 82, 167–183, 1994.CrossRefGoogle Scholar
 Langel, R. A. and W. Hinze, The Magnetic Field of the Earth’s Lithosphere: The Satellite Perspective, Cambridge University Press, 1998.CrossRefGoogle Scholar
 Lesur, V., I. Wardinski, M. Rother, and M. Mandea, GRIMM: the GFZ Reference Internal Magnetic Model based on vector satellite and observatory data, Geophys. J. Int., 173(2), 382–394, 2008.CrossRefGoogle Scholar
 Lesur, V., I. Wardinski, M. Hamoudi, and M. Rother, The second generation of the GFZ Reference Internal Magnetic Model: GRIMM2, Earth Planets Space, 62, this issue, 765–773, 2010.CrossRefGoogle Scholar
 Lowes, F., Spatial power spectrum of the main geomagnetic field and extrapolation to the core, Geophys. J. R. Astron. Soc., 36, 717–730, 1974.CrossRefGoogle Scholar
 Maus, S., S. Macmillan, T. Chernova, S. Choi, D. Dater, V. Golovkov, V. Lesur, F. Lowes, H. Lühr, W. Mai, S. McLean, N. Olsen, M. Rother, T. Sabaka, A. Thomson, and T. Zvereva, The 10thGeneration International Geomagnetic Reference Field, Geophys. J. Int., 161, 561–565, 2005.CrossRefGoogle Scholar
 Maus, S., M. Rother, C. Stolle, W. Mai, S. Choi, H. Lühr, D. Cooke, and C. Roth, Third generation of the Potsdam Magnetic Model of the Earth (POMME), Geochem. Geophys. Geosyst., 7(7), Q07008, 2006.CrossRefGoogle Scholar
 Maus, S., C. Manoj, J. Rauberg, I. Michaelis, and H. Lühr, NOAA/NGDC candidate models for the 11th generation International Geomagnetic Reference Field and the concurrent release of the 6th generation Pomme magnetic model, Earth Planets Space, 62, this issue, 729–735, 2010.CrossRefGoogle Scholar
 Olsen, N. and M. Mandea, Rapidly changing flows in the Earth’s core, Nature Geosci., 1, 390–394, 2008.CrossRefGoogle Scholar
 Olsen, N., T. Sabaka, and F. Lowes, New parametrisation of external and induced fields in geomagnetic field modeling, and a candidate model for IGRF 2005, Earth Planets Space, 57, 1141–1149, 2005.CrossRefGoogle Scholar
 Olsen, N., H. Lühr, T. Sabaka, M. Mandea, M. Rother, L. TøffnerClausen, and S. Choi, CHAOS—A model of Earth’s magnetic field derived from CHAMP, Ørsted, and SACC magnetic satellite data, Geophys. J. Int., 166(1), 67–75, 2006.CrossRefGoogle Scholar
 Olsen, N., M. Mandea, T. Sabaka, and L. TØffnerClausen, CHAOS2–A Geomagnetic field model derived from one decade of continuous satellite data, Geophys. J. Int., 179, 1477–1487, 2009.CrossRefGoogle Scholar
 Olsen, N., M. Mandea, T. J. Sabaka, and L. TØffnerClausen, The CHAOS3 geomagnetic field model and candidates for the 11th generation IGRF, Earth Planets Space, 62, this issue, 719–727, 2010.CrossRefGoogle Scholar
 Sabaka, T., N. Olsen, and M. Purucker, Extending comprehensive models of the Earth’s magnetic field with Ørsted and CHAMP data, Geophys. J. Int., 159, 521–547, 2004.CrossRefGoogle Scholar
 Stolle, C., H. Lühr, M. Rother, and G. Balasis, Magnetic signatures of equatorial spread F as observed by the CHAMP satellite, J. Geophys. Res., 111, A02304, 2006.Google Scholar
 Thébault, E., A. Chulliat, S. Maus, G. Hulot, B. Langlais, A. Chambodut, and M. Menvielle, IGRF candidate models at times of rapid changes in core field acceleration, Earth Planets Space, 62, this issue, 753–763, 2010.CrossRefGoogle Scholar