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T. Erhardt

Publications and source records attributed to T. Erhardt.

2 recordsLinked to original sources

36Cl Concentrations from Polar Ice Cores Set New Constraints on the Carrington Event

The Carrington event of 1859 CE is considered as one of the largest geomagnetic storms of the observational era, and often used as a benchmark for a worst-case scenario. Yet, there exists no robust evidence of an associated solar energetic particle event of a significant magnitude, based on measurements of cosmogenic radionuclides 10Be and 14C from ice cores and tree rings, respectively. In this study, we present two 36Cl records from Greenland with 2-year and 4-year resolution from the EGRIP and NGRIP ice-core sites, together with semi-annual 10Be data from EGRIP, as well as annual 10Be and 36Cl concentrations from the Dome Summit Site, Law Dome, East Antarctica. We observe no significant 36Cl concentration increase around 1859 CE in the three records. This allows us to rule out an extreme solar energetic particle event hitting Earth associated with the Carrington event in terms of fluence above 30 MeV. Based on these ice core 36Cl measurements, we can suggest two scenarios: i) a soft SEP event with a maximum fluence above 30 MeV up to three times larger than any Space Age event or, ii) the possibility that there was no Earth-bound SEP event.

astro-ph.SR

Heavy tailed spatial autocorrelation models

Appropriate models for spatially autocorrelated data account for the fact that observations are not independent. A popular model in this context is the simultaneous autoregressive (SAR) model that allows to model the spatial dependency structure of a response variable and the influence of covariates on this variable. This spatial regression model assumes that the error follows a normal distribution. Since this assumption cannot always be met, it is necessary to extend this model to other error distributions. We propose the extension to the $t$-distribution, the tSAR model, which can be used if we observe heavy tails in the fitted residuals of the SAR model. In addition, we provide a variance estimate that considers the spatial structure of a variable which helps us to specify inputs for our models. An extended simulation study shows that the proposed estimators of the tSAR model are performing well and in an application to fire danger we see that the tSAR model is a notable improvement compared to the SAR model.

stat.ME