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

Earth, Planets and Space

Hinami Taniguchi¹  Futoshi Takahashi¹  Masaki Matsushima²  Shin’ya Nakano³  Hisayoshi Shimizu⁴  Takuto Minami⁵  Ryosuke Nakashima⁶  Hiroaki Toh⁷  Vincent Lesur⁸ 

[1] Department of Earth and Planetary Sciences, School of Science, Kyushu University, Fukuoka, Japan;Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Tokyo, Japan;Department of Statistical Modeling, The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan;Earthquake Research Institute, The University of Tokyo, Tokyo, Japan;Earthquake and Volcano Research Center, Graduate School of Environmental Studies, Nagoya University, D2-2, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi, Japan;Department of Planetology, Graduate School of Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, Hyogo, Japan;Faculty of Science, Kyushu University, Fukuoka, Japan;Graduate School of Science, Kyoto University, Kyoto, Japan;Université de Paris, Institut de physique du Globe de Paris, CNRS, 75005, Paris, France;

关键词: IGRF;    Secular variation;    Geodynamo;    Data assimilation;    Core surface flow;    4DEnVar;   

DOI  :  10.1186/s40623-020-01253-8

来源: Springer

【 摘 要 】

We have submitted a secular variation (SV) candidate model for the thirteenth generation of International Geomagnetic Reference Field model (IGRF-13) using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code. This is the first contribution to the IGRF community from research groups in Japan. A geomagnetic field model derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data, has been used as input data to the assimilation scheme. We adopt an ensemble-based assimilation scheme, called four-dimensional ensemble-based variational method (4DEnVar), which linearizes outputs of MHD dynamo simulation 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 core surface and flow velocity field slightly below the core surface. Dimensionless time of numerical geodynamo is adjusted to the actual time by comparison of secular variation time scales. For SV prediction, we first generate an ensemble of dynamo simulation results from a free dynamo run. We then assimilate the ensemble to the data with a 10-year assimilation window through iterations, and finally forecast future SV by the weighted sum of the future extension parts of the ensemble members. Hindcast of the method for the assimilation window from 2004.50 to 2014.25 confirms that the linear approximation holds for 10-year assimilation window with our iterative ensemble renewal method. We demonstrate that the forecast performance of our data assimilation and forecast scheme is comparable with that of IGRF-12 by comparing data misfits 4.5 years after the release epoch. For estimation of our IGRF-13SV candidate model, we set assimilation window from 2009.50 to 2019.50. We generate our final SV candidate model by linear fitting for the weighted sum of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derive errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members.

【 授权许可】

CC BY