Assimilating geomagnetic data with dynamo simulations: a window to the Earth's core dynamics

地磁场的变化提供了了解地球动态铁核的唯一窗口。我们建议开发一个数据同化框架，将观测和数值发电机模拟结合在一起，以产生一个真实的地球核心动力学模型。虽然数据同化在天气预报中经常使用，但它在地磁方面的应用仍处于初级阶段。特别的挑战在于，地磁数据是在远离核心发电机的地方测量的，而且需要约束复杂过程的数据相对较少。在我们选择的方法--集合卡尔曼滤波中，观测数据和岩心动力学之间的联系是基于几个并行运行的数值发电机模拟的统计相关性。主要目标是a)提供对地球发电机模拟的基本成分的理解，b)开发一个从1900年到现在的整个核心动力学模型，以及c)使用该模型来预测几十年的地磁场演化。广泛的数值研究将有助于确定不同同化技术、发电机模式复杂性、集合大小和计算成本之间的最佳折衷。与其他SPP团队合作，这项工作将有助于建立从磁层电流系统到地球深部运行的发电机过程的地球多样化磁性的最新和综合观点。

Variations in the geomagnetic field provide the only window into the dynamic iron core of planet Earth. We propose to develop a data assimilation framework that combines observations and numerical dynamo simulations in order to yield a realistic model of the Earth’s core dynamics. While data assimilation is routinely used in weather prediction, its application to geomagnetism is still in its infancy. The particular challenges lie in the fact that geomagnetic data are measured far away from the core dynamo and that relatively few data have to constrain a complex process. In our method of choice, the Ensemble Kalman Filter, the link between observations and core dynamics is based on the statistical correlations from several numerical dynamo simulations running in parallel. The main objectives are a) to provide an understanding of the essential ingredients in geodynamo simulations, b) to develop a model for the whole core dynamics from 1900 to today, and c) to use this model for forecasting the geomagnetic field evolution over several decades. Extensive numerical studies will serve to identify the best compromise between different assimilation techniques, dynamo model complexity, ensemble size, and computational costs. In collaboration with other SPP teams, this work will help to establish an up-to-date and integrated view of the Earth’s diverse magnetism from magnetospheric current systems to the dynamo process operating in our planet’s deep core.