A secular variation candidate for IGRF-14 based on core-flow inversion via an ensemble Kalman smoother.

Area of Science:

• Geophysics
• Earth Science
• Computational Science

Background:

• Accurate prediction of Earth's magnetic field secular variation (SV) is crucial for navigation and infrastructure.
• Existing data assimilation (DA) systems often rely on complex models and may not fully capture core dynamics.

Purpose of the Study:

• To develop and validate a candidate mean secular variation (SV) model for 2025.0-2030.0.
• To assess the effectiveness of an Ensemble Kalman Smoother (EnKS) integrated with a frozen-flux core model for geomagnetic forecasting.
• To explore the potential of EnKS in understanding the geodynamo without requiring model adjoints.

Main Methods:

• A data assimilation (DA) system employing a frozen-flux core model and an Ensemble Kalman Filter (EnKF) with an Ensemble Kalman Smoother (EnKS).
• Assimilation of Gauss coefficients from the Kalmag field model to estimate core flow.
• Forecasting of magnetic field changes based on the inferred core flow.

Main Results:

• The EnKS-enhanced DA system produced a superior SV forecast compared to an EnKF-only approach.
• The inferred core flow exhibited structures consistent with established models like the eccentric gyre and westward drift.
• The EnKS methodology proved effective in predicting mean SV over past 5-year periods.

Conclusions:

• The proposed SV model and EnKS methodology offer a promising advancement in geomagnetic forecasting.
• The EnKS system is easily implemented into existing EnKF frameworks, enhancing their predictive capabilities.
• This approach provides a valuable tool for studying the geodynamo, especially for complex 3-D dynamo models.