The workshop will take place at Mines Paris – PSL, in the center of Paris, from May 26 to May 28, 2025.

This international workshop aims to explore cutting-edge methods and emerging challenges in statistical bias correction for climate studies across various time scales – from seasonal forecasts to long-term climate change projections.

This dynamic event will bring together leading climate modelers, developers of innovative bias correction tools (including machine and deep learning techniques), and key users from diverse climate-related fields.

Participants will have the unique opportunity to delve into the latest advancements, share practical insights, and discuss the critical issues surrounding biases in climate models.

Together, we will tackle the evolving challenges in climate services, fostering collaborations that can shape the future of climate science and its applications.

Call for contributions

Information about contributions can be found here.

Registration

Registration will soon be available here.

For payment, please follow instructions and use the dedicated plateform (Azur-Colloque). We will update the payment status on your registration once the payment has been made.

• Early-bird rates (until 16/02/2025) : 140€ (taxes and VAT included)
• Regular rates (until 04/05/2025) : 200€ (taxes and VAT included).

December 2024: Start of registration and abstract submission

16 February 2025: Deadline for registering on early-bird rates

01 April 2025: Deadline for abstract submission

22 April 2025: Notification of acceptance

04 May 2025: Deadline for registering (including payment)

26 - 28 May 2025 : Bias correction in Climate Studies Workshop

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The Paris BC 2025 workshop is jointly organized by the EXTENDING project (TRACCS-PC4, IPSL), the Geo-Learning Chair (INRAE & Mines Paris School – PSL) & the SAMA group of IPSL, in partnership with the University of Cantabria (Spain), the Freie Universität Berlin (Germany) and the University of Lausanne (Switzerland).

This event is organised with the financial support of:

• the French state, through the National Agency for Research (ANR) as part of the France 2030 program (ANR-22-EXTR-0005 grant)
• the GeoLearning chair
  
• the Institut Pierre Simon Laplace (IPSL) through SAMA program (Statistics for Analysis, Modelling and Assimilation)
• the University of Lausanne (Unil) through COMBINE project - hosted by GAIA lab (Geostatistical Algorithms and Image Analysis)