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.
