SOURCE-LINKED INTELLIGENCE

Towards an Integrated Capability to Explain and Predict Regional Climate Changes

CORDIS · observation · Publication date unknown

onal climatic changes, including extremes, on inter-annual to multi-decadal time scales. It will do so by exploiting newly available climate simulations and Earth Observations (EOs), and by combining machine learning (ML) with physical methods. The research will target fundamental knowledge gaps related to atmospheric circulation and land-atmosphere interactions, which represent major limitations in current climate predictions and projections, and in particular in understanding changes in European summer extremes. To underpin the research, and benefitting the wider research community, EXPECT will develop tools to efficiently analyse a variety of large data sets in combination that are hosted in different repositories across institutions. This will facilitate the exploitation of recent investments into high-resolution climate models and EO data. EXPECT will further build data science capacity for the scientifically robust, efficient and reproducible analysis of the massive data assets, including novel ML approaches, and provide training for the climate science community and the next

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recordType

award

status

SIGNED

region

EU

value

6648370

unit

EUR