iMIRACLI · innovative MachIne leaRning to constrain Aerosol-cloud CLimate Impacts (iMIRACLI)

innovative MachIne leaRning to constrain Aerosol-cloud CLimate Impacts (iMIRACLI)

Climate change is one of the most urgent problems facing mankind. Yet, the uncertainty of non-greenhouse gas perturbations (radiative forcing) associated with air pollution and its effect on clouds (aerosol-cloud interactions) limits our understanding of climate sensitivity. Progress has been hampered by the difficulty of disentangling aerosol effects on clouds and climate from their co-variability with confounding factors. Additional challenges are posed by limitations in remote sensing, low signal-to-noise ratios, and computational challenges like the scale and heterogeneity of datasets. Innovative techniques developed by the AI and machine learning community show huge potential but have not yet found their way into climate sciences – and climate scientists are currently not trained to capitalise on these advances. The iMIRACLI ITN built on the hypothesis that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan matched Early Career Researchers (ESRs) with supervisors from climate and data sciences as well as a non-academic advisor and secondment, and provides them with state-of-the-art data and climate science training. Partners from the non-academic sector provide training in a commercial and non-academic research settings. The overall objective of iMIRACLI was to train and shape a new generation of climate data scientists with a solid foundation in climate sciences and competence in the latest machine learning techniques; ideally trained for employment in the academic and commercial sectors. This innovative approach aimed to answer our top-level science question: Can we develop and expand machine learning solutions to the analysis of the exploding amounts of climate data, to deliver a breakthrough in climate research, by tracing and quantifying the impact of aerosol perturbations from the microscale to the imprints on large-scale climate? We addressed this top-level objective through a combination of science questions (SQs) emerging from climate and data sciences. Significant advances have been made in all areas, addressing our top-level objective and all related science questions, which were disseminated through a large number of conference presentations and publications and also presented at an international iMIRACLI workshop on machine learning for climate science at Oxford in June 2024, which also concluded the action.

Data: CORDIS, © European Union