The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of 14 limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias and short-term variability , together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20 and 40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5-12 % and the drifts are at most ±5 % decade −1 (or even ±3 % decade −1 for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions ; we identified biases of 10 % and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY) and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE and possibly GO-MOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.

In this paper we study the extended atmosphere of the late-type supergiant α Orionis. Infrared spectroscopy of red supergiants reveals strong molecular bands, some of which do not originate in the photosphere but in a cooler layer of molecular material above it. Lately, these layers have been spatially resolved by near and mid-IR interferometry. In this paper, we try to reconcile the IR interferometric and ISO-SWS spectroscopic results on α Orionis with a thorough modelling of the photosphere, molecular layer(s) and dust shell. From the ISO and near-IR interferometric observations, we find that α Orionis has only a very low density water layer close above the photosphere. However, mid-IR interferometric observations and a narrow-slit N-band spectrum suggest much larger extra-photospheric opacity close to the photosphere at those wavelengths, even when taking into account the detached dust shell. We argue that this cannot be due to the water layer, and that another source of mid-IR opacity must be present. We show that this opacity source is probably neither molecular nor chromospheric. Rather, we present amorphous alumina (Al 2 O 3 ) as the best candidate and discuss this hypothesis in the framework of dust-condensation scenarios.

Abstract. At Uccle, Belgium, a long time series of simultaneous measurements of erythemal ultraviolet (UV) dose (S ery ), global solar radiation (S g ), total ozone column (Q O 3 ) and aerosol optical depth (τ aer ) (at 320.1 nm) is available, which allows for an extensive study of the changes in the variables over time. Linear trends were determined for the different monthly anomalies time series. S ery , S g and Q O 3 all increase by respectively 7, 4 and 3 % per decade. τ aer shows an insignificant negative trend of −8 % per decade. These trends agree with results found in the literature for sites with comparable latitudes. A change-point analysis, which determines whether there is a significant change in the mean of the time series, is applied to the monthly anomalies time series of the variables. Only for S ery and Q O 3 , was a significant change point present in the time series around February 1998 and March 1998, respectively. The change point in Q O 3 corresponds with results found in the literature, where the change in ozone levels around 1997 is attributed to the recovery of ozone. A multiple linear regression (MLR) analysis is applied to the data in order to study the influence of S g , Q O 3 and τ aer on S ery . Together these parameters are able to explain 94 % of the variation in S ery . Most of the variation (56 %) in S ery is explained by S g . The regression model performs well, with a slight tendency to underestimate the measured S ery values and with a mean absolute bias error (MABE) of 18 %. However, in winter, negative S ery are modeled. Applying the MLR to the individual seasons solves this issue. The seasonal models have an adjusted R 2 value higher than 0.8 and the correlation between modeled and measured S ery values is higher than 0.9 for each season. The summer model gives the best performance, with an absolute mean error of only 6 %. However, the seasonal regression models do not always represent reality, where an increase in S ery is accompanied with an increase in Q O 3 and a decrease in τ aer . In all seasonal models, S g is the factor that contributes the most to the variation in S ery , so there is no doubt about the necessity to include this factor in the regression models. The individual contribution of τ aer to S ery is very low, and for this reason it seems unnecessary to include τ aer in the MLR analysis. Including Q O 3 , however, is justified to increase the adjusted R 2 and to decrease the MABE of the model.

Key Points (shortened to less than 140 characters each, and changed as suggested by Reviewer #2): • In spring and summer 2020, stations in the northern extratropics report on average 7% (4 nmol/mol) less tropospheric ozone than normal. • Such low tropospheric ozone, over several months, and at so many sites, has not been observed in any previous year since at least 2000. • Most of the reduction in tropospheric ozone in 2020 is likely due to emissions reductions related to the COVID-19 pandemic.

Abstract. Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of groundbased instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2).This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.