Forecasting secular variation using core flows

Over the past ten years satellite measurements in combination with data from ground-based observatories have allowed very detailed models of the secular variation (SV) of the Earth’s magnetic field to be constructed. However, forecasting the change of the main field still remains a challenge, primarily because the core processes controlling SV are not sufficiently well understood. Hence, most forecasts do not appeal to any physical modelling constraints but use, for example, polynomial extrapolation from previous measurements. We attempt to apply a physical model to forecast the average SV during 2010–2015 by developing a core flow model. This steady flow model, derived from SV data during 2004.5 to 2009.5, generates a set of Gauss SV coefficients which are used to advect the large scale magnetic field forwards in time. Although this model has not been submitted as a candidate for IGRF-11, we present our SV prediction model and compare it to other candidate IGRF-11 SV models. In addition, we examine the use of the Ensemble Kalman filter to optimally assimilate field models derived from (1) forecast methods and (2) noisy data measurements. Such a scenario might conceivably arise if high quality satellite data with global coverage are not available for a significant period of time. We show that the overall misfit of the assimilated model to the actual field can be lower than the individual misfits of the input models, provided the uncertainties of each model are reasonably well known.