Aerosol-cloud interactions (ACI) remain the largest source of uncertainty in past, present, and future radiative forcing, impeding
credible climate projections. ACI effects are expected to change dramatically as we enter a post-fossil world, characterized by strong
reductions in anthropogenic aerosol emissions but with increasingly larger impacts from natural aerosols. Although we expect
cleaner clouds compared to today, ACI in this post-fossil state may considerably differ from preindustrial conditions, owing to shifts in
climate and changes in sources region characteristics. CleanCloud will address the major gaps impeding robust ACI assessments,
improve their representation in current and next generation kilometer-scale climate models , quantify and understand their regional
and temporal effects, and how they will evolve in the transition to the post-fossil regime.

To accomplish this, CleanCloud will 1) carry
out targeted field experiments in European climate hotspots; 2) develop state-of-the-art algorithms and analysis tools to obtain new
proxies and diagnostics for key ACI-related processes; 3) contribute to the calibration and validation of upcoming satellite missions in
coordination with the satellite community; 4) improve and better constrain kilometer- and large-scale climate models using advanced
machine learning, data assimilation and model calibration, confronting perturbed physics ensembles with existing and new satellite
and in-situ data; and 5) assess the role of aerosols in the life cycle of convective systems, focusing on precipitation formation and the
impacts on the hydrological cycle, and 6) enhance the exploitation of data centres, measurement programs, international campaigns,
laboratory studies, and models. With these, CleanCloud will profoundly strengthen European Research on climate change,
significantly contribute to upcoming climate assessments, and benefit society through models that enable improved weather and
seasonal predictions.

The scope of the project is to use the spectra measured by the SkySpec-2D - DOAS systems in San Pietro Capofiume (SPC, BO), and in Tor Vergata (Rome) to retrieve HCHO vertical cloumn densities (VCDs). The HCHO VCDs retrieved at SPC will be used together with NO2 VCDs to inverstigate their ratio woth respect to O3 values at surface, while the HCHO VCDs retrieved at Tor Vergata will be compared to the ones measured by the co-located Pandora instruments. In both cases comparisons with TROPOMI data will be performed.

Sun photometers are able to retrieve AOD only under cloudless (sun not obscured by clouds) conditions. Long term automated measurements use algorithms in order to identify and eliminate data affected by such cloud presence. Most algorithms are based on defining the limitations of short term changes of the radiative fields that can be attributed only to aerosols. We plan to try to combine instruments measuring at different frequencies and spectral resolutions in order to improve such algorithms especially in cases of clouds with low radiation related variability (e.g. cirrus) and aerosol cases with high variability (dust, smoke, pollution episodes etc), where the related uncertainties are higher. Also to use campaign based data from different instruments with different processing algorithms and measurement frequencies (e.g. CIMEL ~15min, PFR ~1 min) , in order to identify cloud elimination algorithm related advantages but also disadvantages.