Narrowing uncertainties of climate projections using data science tools? -Pierre Tandeo

The seminar is on March the 26th, at 10 o’clock and will be held remotely, in english.

The slides can be found here. We are currently uploading the video of the talk and will be adding a link to it as soon as it uploads.

Link to the zoom session: https://zoom.us/j/94170014183

Pierre Tandeo’s presentation is entitled:

« Narrowing uncertainties of climate projections using data science tools? »

Abstract:

 Climate indices show large variability in CMIP climate predictions. In this presentation, we propose to weight multi-model climate simulations to reduce the uncertainty in climate predictions, and better estimate the future evolution of climate indices. The proposed methodology is based on advanced data science tools (i.e, data assimilation, analog forecasting, model evidence metrics), to accurately compute distances between current observations and simulated climate indices. This low-cost procedure is tested on a simplified climate model. The results show that the methods can be applied locally and is able to identify relevant parameterizations.

Short bio:

Pierre Tandeo an associate professor at IMT Atlantique (Brest, France) and an associate researcher at the Data Assimilation Research Team, RIKEN Center for Computational Science (Kobe, Japan). More information:  https://tandeo.wordpress.com/.

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