Co-PIs:

Reto Knutti (ETH Zurich)

Nicolai Meinshausen (ETH Zurich)

Problem:

Provide a new assessment of changes in the water cycle by advancing Detection&Attribution (D&A) of climate change using data science techniques.

Solution:

New statistical techniques for D&A using machine learning and causal inference

Advancing climate science techniques for D&A

Development of multivariate attribution techniques

Incorporating data science into dynamical adjustment methodologies

Code sharing, reproducibility, publications

Impact:

Advance D&A techniques using data science.

Guide climate model development based on the agreement or mismatch of models and observations.