Causal modeling in ES
See:
# Resources
# Talks
- #TALK Causal Networks as a framework for climate science to improve process understanding (Marlene Kretschmer)
- #TALK Machine Learning in Climate Science: Finding causal connections and improving seasonal forecasts (Dim Coumou)
# Code
- #CODE
Tigramite - Tigramite is a time series analysis python module for causal discovery
- https://jakobrunge.github.io/tigramite/
- PCMCI algorithm. Causal discovery
- #CODE
RGCPD - Investigate teleconnections, test for causality, and make forecasts
- RG-CPD is a framework to process 3-dimensional climate data, such that relationships based on correlation can be tested for conditional independence, i.e. causality
- https://zenodo.org/record/1486739#.X8Y0emT0mx0
# References
- #PAPER Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation (Kretschmer 2016)
- #PAPER Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data (Chalupka 2016)
- #PAPER Early prediction of weak stratospheric polar vortex states using causal precursors (Kretschmer 2017)
- #PAPER
Detecting causal associations in large nonlinear time series datasets (Runge 2019)
- https://advances.sciencemag.org/content/5/11/eaau4996
- PCMCI algorithm
- #PAPER Inferring causation from time series in Earth system sciences (Runge 2019)
- #PAPER Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach (Krich 2020)
- #PAPER
Causal networks for climate model evaluation and constrained projections (Nowack 2020)
- apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses
- the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation
- #PAPER Reconstructing regime-dependent causal relationships from observational time series (Saggioro 2020)
- #PAPER Dominant patterns of interaction between the tropics and mid-latitudes in boreal summer: causal relationships and the role of timescales (Di Capua 2020)
- #PAPER Learning Granger Causal Feature Representations (Varando 2021)