Abstract. Dust aerosols are very efficient ice nuclei, important for heterogeneous cloud glaciation even in regions distant from desert sources. A new generation of ice nucleation parameterizations, including dust as an ice nucleation agent, opens the way towards a more accurate treatment of cold cloud formation in atmospheric models. Using such parameterizations, we have developed a regional dust-atmospheric modelling system capable of predicting, in real time, dustinduced ice nucleation. We executed the model with the added ice nucleation component over the Mediterranean region, exposed to moderate Saharan dust transport, over two periods lasting 15 and 9 days, respectively. The model results were compared against satellite and ground-based cloud-icerelated measurements, provided by SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and the CNR-IMAA Atmospheric Observatory (CIAO) in Potenza, southern Italy. The predicted ice nuclei concentration showed a reasonable level of agreement when compared against the observed spatial and temporal patterns of cloud ice water. The developed methodology permits the use of ice nuclei as input into the cloud microphysics schemes of atmospheric models, assuming that this approach could improve the predictions of cloud formation and associated precipitation.

Abstract. There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA-CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is "correct". Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA-CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA-CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.

Abstract. In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere) and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere) and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr) than for clouds (20–35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Long-term homogeneous climate data records (CDRs) are essential to diagnose changes in our climate, understand its variability, and assess and contextualize future climate projections (Cramer et al., 2018). Use of CDRs influenced by residual non-climatic factors may lead to incorrect conclusions about the changing state of the climate (Kivinen et al., 2017). Therefore, when CDRs are used it is highly desirable to:

Abstract. Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60∘ N, 15.72∘ E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016. The intercomparison highlighted that the MiniMPL range-corrected signals (RCSs) show, on average, a fractional difference with respect to those of CNR-IMAA Atmospheric Observatory (CIAO) lidars ranging from 5 to 15 % below 2.0 km a.s.l. (above sea level), largely due to the use of an inaccurate overlap correction, and smaller than 5 % in the free troposphere. For the CL51, the attenuated backscatter values have an average fractional difference with respect to CIAO lidars < 20–30 % below 3 km and larger above. The variability of the CL51 calibration constant is within ±46 %. For the CS135, the performance is similar to the CL51 below 2.0 kma.s.l., while in the region above 3 kma.s.l. the differences are about ±40 %. The variability of the CS135 normalization constant is within ±47 %. Finally, additional tests performed during the campaign using the CHM15k ceilometer operated at CIAO showed the clear need to investigate the CHM15k historical dataset (2010–2016) to evaluate potential effects of ceilometer laser fluctuations on calibration stability. The number of laser pulses shows an average variability of 10 % with respect to the nominal power which conforms to the ceilometer specifications. Nevertheless, laser pulses variability follows seasonal behavior with an increase in the number of laser pulses in summer and a decrease in winter. This contributes to explain the dependency of the ceilometer calibration constant on the environmental temperature hypothesized during INTERACT.