Propagated random, systematic, and sampling uncertainties in GNSS radio occultation climate time series

GNSS radio occultation (RO) provides high-quality atmospheric profiles of variables such as temperature and pressure. Recent efforts have succeeded in propagating the related systematic and random error effects from the raw measurements to the resulting profiles, attaching a measure of observational uncertainty to each one. In this work we build upon these profile-level uncertainty estimates and propagate them to aggregated mean fields for climate applications. In this context, sampling uncertainties also need to be considered. This approach is applied to the GNSS RO time series of refractivity, dry temperature, and physical temperature. The results show that random and residual sampling uncertainties decrease with increasing aggregation size and are comparable in magnitude. They dominate refractivity uncertainty at small aggregation scales and contribute substantially to temperature uncertainty. Systematic uncertainty is the main source of uncertainty for refractivity at larger aggregation scales, as well as for pressure and dry temperature at commonly used aggregation sizes. Uncertainties exhibit strong spatial variability, with the largest values occurring in polar regions. There are also substantial, mission-dependent variations within the time series.