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Abstract An intriguing aspect of the famous September 2, 1859 geomagnetic disturbance (or “Carrington” event) is the horizontal magnetic (B_H) data set measured in Colaba, India (magnetic latitude approximately 20°N). The field exhibits a sharp decrease of over 1,600 nT and a quick recovery of about 1,300 nT, all within a few hours during the daytime. The mechanism behind this has previously been attributed to magnetospheric processes, ionospheric processes or a combination of both. In this study, we outline our efforts to replicate this low‐latitude magnetic field using the Space Weather Modeling Framework. By simulating an extremely high pressure solar wind scenario, we can emulate the low‐latitude surface magnetic signal at Colaba. In our simulation, magnetospheric currents adjacent to the near‐Earth magnetopause and strong Region 1 field‐aligned currents are the main contributors to the large ColabaB_H. The rapid recovery ofB_Hin our simulated scenario is due to the retreat of these magnetospheric currents as the magnetosphere expands, as opposed to ring current dynamics. In addition, we find that the scenario that best emulated the surface magnetic field observations during the Carrington event had a minimum calculated Dst value between −431 and −1,191 nT, indicating that Dst may not be a suitable estimate of storm intensity for this kind of event. 

Abstract Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.