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First IS-ENES autumn school on climate data use for impact and adaptation assessments

When
Oct 26, 2020 09:00 AM to Oct 30, 2020 10:00 AM (Europe/Vienna / UTC100)
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The first IS-ENES autumn school on ‘Climate data use for Impact and adaptation assessments’, which will comprise 5 days, is organized in Autumn 2020 (October-November) in the Czech Republic.

During the school, climate scientists and climate impact researchers will interact on topics like climate models, climate data and the creation of new assessment tools.  

The exact time and location will be announced within a few months on this webpage and through the IS-ENES mailing list.

The school aims at:

  • Giving Vulnerability, Impact and Adaptation researchers, climate services providers, etc. basic knowledge for working with climate data and introduce them to the tools to access, visualize and process climate data
  • Enhancing interaction between climate scientists and impact researchers
  • Making knowledge of the IS-ENES consortium available for young scientists all over Europe

 

The school adresses:

  • Vulnerability, Impact, Adaptation (VIA) researchers
  • Climate service providers
  • Climate researchers

 

Requirements for candidates: 

  • MSc diploma, preferably in the natural sciences
  • Some experience with climate data and large data sets
  • We aim for a mix of participants from the different target groups
  • People from Eastern Europe are encouraged to participate

 

Number of participants: 30

Costs: There is no fee for participation. Accommodation and meals are (partly) subsidized.

Set-up of the autumn school

  • Lectures from IS-ENES participants on climate modelling, climate data use, impact modelling, uncertainties, climate services, communication with stakeholders, etc.
  • Half-day field trip/excursion
  • Working on case studies

 

Interested?

If you’re interested, you can send an e-mail to judith.klostermann[a]wur.nl to receive more information on the school and on registration when available.

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IS-ENES3 has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824084