Description du projet
L’apprentissage automatique pour renforcer les connaissances sur les répercussions de l’interaction entre les aérosols et les nuages
L’accord de Paris sur le climat représente un grand pas en avant dans la résolution du problème lié au changement climatique. Toutefois, un vaste éventail de difficultés surgissent dans sa mise en œuvre. L’un des principaux obstacles est le manque de preuves scientifiques sur le forçage de gaz différents des gaz à effet de serre touché par les interactions entre les aérosols et les nuages. Même si la science des mégadonnées a été employée pour mieux comprendre les interactions climatiques entre les aérosols et les nuages, l’intelligence artificielle (IA) et l’apprentissage automatique ne sont pas encore totalement appliqués dans les sciences climatiques et les scientifiques ne sont pas suffisamment formés. Le projet iMIRACLI, financé par l’UE, propose la fusion de l’IA, de l’apprentissage automatique et des sciences climatiques pour étudier les données actuelles et renforcer nos connaissances sur les répercussions de l’interaction aérosols-nuages. Le projet formera des chercheurs en début de carrière pour obtenir une nouvelle génération d’experts en données climatiques.
Objectif
Climate change is one of the most urgent problems facing mankind. Implementation of the Paris climate agreement relies on robust scientific evidence. Yet, the uncertainty of non-greenhouse gas forcing associated with aerosol-cloud interactions limits our constraints on climate sensitivity. Radically new ideas are required. While the majority of forcing estimates are model based, model uncertainties remain too large to achieve the required uncertainty reductions. The quantification of aerosol cloud climate interactions in Earth Observations is thus one of the major challenges of climate science. Progress has been hampered by the difficulty to disentangle aerosol effects on clouds and climate from their covariability with confounding factors, limitations in remote sensing, very low signal-to-noise ratios as well as computationally, due to the scale of the big (>100Tb) datasets and their heterogeneity. Such big data challenges are not unique to climate science but occur across a wide range of data science applications. 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 central hypothesis of IMIRACLI is that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and hence advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan will match each ESR with supervisors from climate and data sciences as well as a non-academic advisor and secondment and provide them with state-of-the-art data and climate science training. Partners from the non-academic sector will be closely involved in each of the projects and provide training in a commercial context. This ETN will produce a new generation of climate data scientists, ideally trained for employment in the academic and commercial sectors.
Champ scientifique
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyenvironmental engineeringremote sensing
- social scienceseconomics and businessbusiness and managementemployment
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Mots‑clés
Programme(s)
Régime de financement
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinateur
OX1 2JD Oxford
Royaume-Uni