Abstract
We have produced a 5-year mean secular variation (SV) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been submitted as a candidate predictive model of IGRF-13 for the period 2020–2025.Graphical abstract
Highlights
It has long been observed that the Earth’s main magnetic field changes slowly in time (Bullard et al 1950; Hide 1967; Bloxham and Gubbins 1985; Jackson et al 2000; Lund 2018)
It remains computationally prohibitive to simulate the geodynamo in the parameter regimes appropriate for the Earth’s core, the asymptotic properties emerged from numerical solutions with wide range of dynamo parameter values open the door for quantitative applications of numerical models to the geomagnetic field (e.g. Christensen and Aubert 2006; Christensen et al 2010; Aubert and Fournier 2017; Kuang et al 2017)
The development of ensemble forecasting techniques has contributed to the secular variation (SV) forecasts, due to the ability to estimate uncertainty, which is newly available with this geomagnetic forecast
Summary
It has long been observed that the Earth’s main magnetic field changes slowly in time (Bullard et al 1950; Hide 1967; Bloxham and Gubbins 1985; Jackson et al 2000; Lund 2018). It remains computationally prohibitive to simulate the geodynamo in the parameter regimes appropriate for the Earth’s core, the asymptotic properties emerged from numerical solutions with wide range of dynamo parameter values open the door for quantitative applications of numerical models to the geomagnetic field (e.g. Christensen and Aubert 2006; Christensen et al 2010; Aubert and Fournier 2017; Kuang et al 2017) One such application is to use a numerical model to predict geomagnetic SV on time scales of several years and longer, if proper initialization is made using data assimilation techniques, as is done in numerical weather prediction (NWP). Where ris the radial unit vector, and TB , PB are the toroidal and poloidal scalars and are described in GEMS as
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