Descripción del proyecto
Usar el aprendizaje automático para aumentar el conocimiento sobre el impacto de los aerosoles en las nubes
El Acuerdo de París representa un gran paso hacia la resolución del problema del cambio climático. Sin embargo, a la hora de implementarlo, surge una amplia gama de dificultades. Uno de los principales obstáculos es la falta de evidencia científica sobre cómo los gases que no participan en el efecto invernadero se ven afectados por las interacciones entre aerosoles y nubes. Aunque se ha utilizado la ciencia de los datos masivos para comprender mejor las interacciones climáticas entre los aerosoles y las nubes, la inteligencia artificial (IA) y el aprendizaje automático todavía no se aplican por completo en la ciencia climática, y los científicos no están correctamente formados. El proyecto iMIRACLI, financiado con fondos europeos, propone la unión de la IA, el aprendizaje automático y la ciencia climática con el fin de estudiar los datos existentes y aumentar nuestros conocimientos sobre el impacto de los aerosoles en las nubes. El proyecto formará a investigadores noveles para producir una nueva generación de expertos en datos climáticos.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinador
OX1 2JD Oxford
Reino Unido