Clouds and climate transitioning to post-fossil aerosol regime

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.