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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Докторантска мрежа2020–2024

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2020-01-01 → 2024-06-30
Финансиране от ЕС
4 185 717 €
Участници
9
Схема
MSCA-ITN

Линиите свързват координатора с партньорите.

Накратко на български

Взаимодействието между въздушното замърсяване и облаците се анализира чрез методи за машинно обучение. Това помага за по-доброто разбиране на климатичните промени и чувствителността на климата към външни влияния.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз