Data assimilation uncertainty varies greatly based on the quality and amount of ingested observations and the uncertainty in the background forecast. Ensemble data assimilation (EDA) schemes quantify this combined uncertainty. We investigate climatologically what governs this uncertainty on daily to synoptic time scales, but isolating the effects of changes to the observation system from the truly flow-dependent part of the uncertainty is not straightforward.</p> <p>Drawing on the EDA produced for the ECMWF 5th Generation Reanalysis product (ERA5), we isolate the long- from the short-time-scale uncertainty components using grid-point-wise statistical models. We investigate the patterns and causes of the short time-scale component for a set of weather regimes and the occurrence of warm conveyor belts (WCBs) in the European North Atlantic sector. This provides coherent regions of higher and lower assimilation uncertainty in different weather regimes that can be explained by a combination of mathematical and physical arguments. Moisture presence is a key contributor for elevated assimilation uncertainty and our results indicate that this influence is mediated largely through state-dependent observation uncertainties in addition to model uncertainties usually considered to govern sub-daily error growth.</p> <p>Consequently, we argue that what is commonly conceived as a model or weather system specific uncertainty (the "error-of-the-day") in ERA5 reanalysis is actually highly influenced by derived instrument uncertainties and assimilation challenges. Given ERA5's wide use as well as the role of EDA in providing initial ensemble forecast spread, considering the dynamics of assimilation uncertainty should be standard practice.