SUMMER SCHOOL: ARTIFICIAL INTELLIGENCE FOR DETECTION AND ATTRIBUTION OF CLIMATE EXTREMES

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Dates: 20 June – 2 July 2022

Location: ICTP, Trieste, Italy

Format: Hybride (face-to-face or virtual)

 

Grants: A limited number of grants are available to support the attendance of selected participants, with priority given to participants from developing countries. There is no registration fee.

Organizers:

Emanuele BEVACQUA (Helmholtz Centre for Environmental Research – UFZ, Germany)

Erika COPPOLA (ICTP, Italy)

Dim COUMOU (Vrije Universiteit Amsterdam, Netherlands)

Davide FARANDA (LSCE-IPSL, CNRS, France)

Aglae JEZEQUEL (LMD, ENS Paris, France)

Robert VAUTARD (LSCE-IPSL, CNRS, France)

Mathieu VRAC (LSCE-IPSL, CNRS, France)

Pascal YIOU (LSCE-IPSL, CEA, CNRS, France)

During the last 5-10 years, a large number of extreme weather and climate events in Europe and worldwide have occurred, causing damage to infrastructure and casualties especially in developing countries. This has raised the question about the role of climate change in altering the odds or the magnitude of a number of such events and the new “science of attribution” has began with several attribution published all around the world. The aim of the school is to define techniques to tackle the problem of attributing meteorological extreme events to climate change by mean of  machine learning technologies. Lectures will also focus on determining causal links of extreme events with the underlying climate dynamics as the atmospheric circulation. The school will also discuss and provide the bases for communicating attribution results to the general public, stakeholders and other scientists in an exact although non specialist language
Topics:
  • Dynamics and thermodynamics of extreme events (including heatwaves, cold spells, severe convective events, tropical and extra-tropical cyclones, compound extremes at  different scales)
  • Statistical tools for extreme event attribution (including rare events algorithms, compound climate extremes, storylines, casual inference, downscaling and bias correction)
  • Machine Learning for extreme event attribution (including phyisics-aware machine learning, explainable artificial intelligence for climate sciences, casual discovery algorithms for extreme events)
  • Outreach and communication training (including a creative writing workshop, communication of extreme event attribution to the general public, school outreach activities and outreach videogames)

Speakers:
E. BARNES, Colorado State U, USA
E. BEVACQUA, UFZ Leipzig, Germany
G. CAMPS-VALLS, ISP-UVEG, Spain
E. COPPOLA, ICTP, Italy
E. COUGHLAN DE PEREZ, RCCC, Netherlands
D. COUMOU, VU Amsterdam, Netherlands
D. FARANDA, LSCE CNRS, France
M.A. FERNÁNDEZ-TORRES, ISP-UVEG, Spain
E. FISCHER, ETH Zurich, Switzerland
L. FRASER, Met Office, UK
A. JEZEQUEL, IPSL-LMD, France
S. KLEIN, OCE, France
M. KRETSCHMER, U Reading, UK
G. MESSORI, Uppsala U, Sweden
F. OTTO, Imperial College, UK
J. RUNGE, DLR, Germany
T. SHEPHERD, U Reading, UK
R. SINGH, RCCC, Netherlands
S. SIPPEL, ETH Zurich, Switzerland
P. SUAREZ, RCCC, Netherlands
R. VAUTARD, IPSL CNRS, France
M. VRAC, LSCE, France
P. YIOU, LSCE, France

Participants are encouraged to submit abstracts for contributed talk. A number of short oral presentation slots will be available for some of them upon selection.

 

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