The thirteenth generation of International Geomagnetic Reference Field (IGRF-13) was released by International Association of Geomagnetism and Aeronomy (IAGA) in December, 2019. Prior to the release, we submitted a secular variation (SV) candidate model for IGRF-13 using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code (Minami et al. submitted to EPS special issue for IGRF-13). Our candidate SV model was evaluated by IAGA Division V Working Group V-MOD and contributed to the final IGRF-13SV model with the optimized weight. This became the first contribution to the IGRF community from research groups in Japan. This was enabled by bilateral corroboration between Japan and France; in our data assimilation scheme, we used the French main field model (Ropp et al. 2020), which was developed from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data. We adopted an iterative assimilation algorithm based on four-dimensional ensemble-based variational method (4DEnVar) (Nakano 2020), which linearizes outputs of our MHD dynamo simulation (Takahashi 2012; 2014) with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the Earth’s core surface, and flow velocity field slightly below the core surface, which was calculated by presuming magnetic diffusion in the boundary layer and tangentially magnetostrophic flow below it (Matsushima 2020). Dimensionless time of numerical geodynamo was adjusted to the actual time by comparison of secular variation time scales. For estimation of our IGRF-13SV candidate model, we first generated an ensemble of dynamo simulation results from a free dynamo run. We then assimilated the ensemble to the data with a 10-year assimilation window from 2009.50 to 2019.50 through iterations, and finally forecasted future SV by linear combination of the future extension parts of the ensemble members. We generated our final SV candidate model by linear fitting for the best linear combination of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derived errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members. In the presentation, we plan to report our IGRF project through the bilateral corroboration with France, and describe our SV candidate model.
The International Geomagnetic Reference Field (IGRF) is a standard mathematical description in terms of spherical harmonic coefficients, known as the Gauss coefficients, for the Earth’s main magnetic field and its secular variation. On December 19, 2019, the working group V-MOD of the International Association of Geomagnetism and Aeronomy (IAGA) released the 13th generation of IGRF, which consists of three constituents; a Definitive IGRF (DGRF) for 2015, an IGRF for 2020, and a secular variation (SV) model from 2020 to 2025. We submitted a candidate model for SV from 2020 to 2025, relying on our strong points, such as geodynamo numerical simulation, data assimilation, and core surface flow modeling.We can estimate core flow near the core-mantle boundary (CMB)from distribution of geomagnetic field and its secular variation. Such a flow model can be obtained for actual physical parameters of the Earth. However, numerical simulations of geodynamo were carried out for physical parameters far from actual ones. Therefore, a core flow model to be used for data assimilation had to be obtained on a condition relevant to the numerical simulations. To obtain the candidate model for SV, we adjusted time-scale of a geodynamo model (Takahashi 2012, 2014) to that of actual SV of geomagnetic field as given by Christensen and Tilgner (2004).In this presentation, we first investigate temporal variations of geomagnetic field due to the magnetic diffusion only. Next, we investigate temporal variations of geomagnetic field due to the motional induction caused by some core flow models as well as the magnetic diffusion. Then we compare secular variations of geomagnetic field forecasted by these methods.
1 0 0 0 OA Inferring geomagnetic secular variation using MHD/kinematic dynamo modeling with data assimilation
Secular variation (SV) of the Earth's magnetic field is governed by the advection and diffusion processes of the magnetic field within the fluid outer core. The IGRF (International Geomagnetic Reference Field) offers the average SV for the next five years to come, which has been estimated in various methods. In general, forecasting the evolution of a non-linear system like the geodynamo in the Earth's core is an extremely difficult task, because the magnetic field generation processes are controlled by the complex interaction of the core flows and the generated magnetic field. Data assimilation has been a promising scheme forecasting the geomagnetic SV as demonstrated in literatures (Kuang 2010, Fournier et al. 2015), where time dependency is controlled by a numerical dynamo model. While Ensemble Kalman Filter (EnKF) has been a popular method for data assimilation in geomagnetism, we apply a different data assimilation procedure, that is, four-dimensional, ensemble-based variational scheme, 4DEnVar. Applying the 4DEnVar scheme iteratively, we have derived a candidate SV model for the latest version of the IGRF. In evaluating SV, two forecasting strategies are tested, in which core flows are assumed to be steady or time-dependent. The former approach is favored in Fournier et al. (2015), where the magnetic field evolves kinematically by the flows prescribed to be time-independent in the initialization step. On the other hand, we have adopted linear combination of magnetohydrodynamic (MHD) models to construct a candidate as the best forecast (Minami et al. 2020). It is likely that which strategy is more suitable to forecasting SV depends on assimilation scheme and/or numerical dynamo model. However, we have little knowledge on the issue at present. In this study, we investigate results of MHD and kinematic dynamo runs with a 4DEnVar scheme in order to have a grasp of the properties of the scheme in the 5-year forecast process. Also, MHD and kinematic runs are compared to infer internal dynamics responsible for SV in the geomagnetic field.