Contribution of proton and electron precipitation to the observed electron concentration in October–November 2003 and September 2005
Abstract. Understanding the altitude distribution of particle precipitation forcing is vital for the assessment of its atmospheric and climate impacts. However, the proportion of electron and proton forcing around the mesopause region during solar proton events is not always clear due to uncertainties in satellite-based flux observations. Here we use electron concentration observations of the European Incoherent Scatter Scientific Association (EISCAT) incoherent scatter radars located at Tromsø (69.58° N, 19.23° E) to investigate the contribution of proton and electron precipitation to the changes taking place during two solar proton events. The EISCAT measurements are compared to the results from the Sodankylä Ion and Neutral Chemistry Model (SIC). The proton ionization rates are calculated by two different methods – a simple energy deposition calculation and the Atmospheric Ionization Model Osnabrück (AIMOS v1.2), the latter providing also the electron ionization rates. Our results show that in general the combination of AIMOS and SIC is able to reproduce the observed electron concentration within ± 50% when both electron and proton forcing is included. Electron contribution is dominant above 90 km, and can contribute significantly also in the upper mesosphere especially during low or moderate proton forcing. In the case of strong proton forcing, the AIMOS electron ionization rates seem to suffer from proton contamination of satellite-based flux data. This leads to overestimation of modelled electron concentrations by up to 90% between 75–90 km and up to 100–150% at 70–75 km. Above 90 km, the model bias varies significantly between the events. Although we cannot completely rule out EISCAT data issues, the difference is most likely a result of the spatio-temporal fine structure of electron precipitation during individual events that cannot be fully captured by sparse in situ flux (point) measurements, nor by the statistical AIMOS model which is based upon these observations.