The SPARC water vapour assessment II:Profile-to-profile comparisons of stratospheric and lowermesospheric water vapour data sets obtained from satellites

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesosphericwater vapour were performed considering 33 data setsderived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data setspecific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 hPa and 5 hPa. Typically, they range from 0.25 ppmv to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases are overall increasing with altitude but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 ppmv and 1 ppmv (4 % to 20 %). Obvious data setspecific bias issues are found for a number of data sets. In our work we performed a drift analysis for data setsoverlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data setsthat are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 hPa to 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 ppmv decade −1 and 0.3 ppmv decade −1 . Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. Like for the biases, a number of data setsexhibit prominent drift issues. In our analyses we found that the large number of MIPAS data setsincluded in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data setsexhibit a relative similarity with respect to the remaining data sets, despite that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.