Ideally, every validation study based on comparisons with ground-based reference data should investigate whether the comparison statistics (bias or mean difference, spread on the differences, drift, etc.) are compatible with the reported random and systematic measurement uncertainties, while taking into account the additional uncertainties due to spatiotemporal sampling and smoothing differences, i.e. non-perfect co-location of the airmasses sensed by both instruments. Indeed, it is only in a few particular cases possible to adopt co-location criteria that result in a sufficiently large number of co-located pairs, while at the same time keeping the impact of atmospheric variability on the comparisons (due to spatiotemporal mismatches) well below the measurement uncertainties. In all other cases, the discrepancy between two data sets will contain non-negligible terms arising from sampling and smoothing differences, which need to be taken into account.  In fact, such an analysis is essential to fully assess the data quality and its fitness-for-purpose, but in practice, it is rarely performed, as this co-location mismatch is hard to quantify reliably. Some pioneering work was published by Cortesi et al. (2007) on uncertainty budget closure for MIPAS/ENVISAT ozone profile validation, by Ridolfi et al. (2007) for the case of MIPAS/ENVISAT temperature profiles validation, by Fasso et al. (2013) in the context of radiosonde intercomparisons, by Lambert et al. (2012) on water vapour comparisons, and by Verhoelst et al. (2015) for GOME-2/MetOp-A total ozone column validation. However, no such studies have hitherto been performed for most other ECVs and/or instruments. This gap therefore concerns the need for (1) further research dealing with methods to quantify co-location mismatch, and (2) governance initiatives to include in the common practices among validation teams dedicated efforts to construct full uncertainty budgets, and use these in the consistency checks.