  Various studies identified possible drivers of extremes of Arctic sea ice
reduction, such as observed in the summers of 2007 and 2012, including
preconditioning, local feedback mechanisms, oceanic heat transport and the
synoptic- and large-scale atmospheric circulations. However, a robust
quantitative statistical analysis of extremes of sea ice reduction is hindered
by the small number of events that can be sampled in observations and numerical
simulations with computationally expensive climate models. Recent studies
tackled the problem of sampling climate extremes by using rare event
algorithms, i.e., computational techniques developed in statistical physics to
reduce the computational cost required to sample rare events in numerical
simulations. Here we apply a rare event algorithm to ensemble simulations with
the intermediate complexity coupled climate model PlaSim-LSG to investigate
extreme negative summer pan-Arctic sea ice area anomalies under pre-industrial
greenhouse gas conditions. Owing to the algorithm, we estimate return times of
extremes orders of magnitude larger than feasible with direct sampling, and we
compute statistically significant composite maps of dynamical quantities
conditional on the occurrence of these extremes. We find that extremely low sea
ice summers in PlaSim-LSG are associated with preconditioning through the
winter sea ice-ocean state, with enhanced downward longwave radiation due to an
anomalously moist and warm spring Arctic atmosphere and with enhanced downward
sensible heat fluxes during the spring-summer transition. As a consequence of
these three processes, the sea ice-albedo feedback becomes active in spring and
leads to an amplification of pre-existing sea ice area anomalies during summer.
