Event Details

DSNE seminar with Peer Nowack, University of East Anglia

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here I will present recent work on causal discovery algorithms as a novel approach for process-oriented climate model evaluation [1,2].

Following an introduction to the concept of causal discovery, I will move on to key scientific implications of this new approach when applied to global sea level pressure datasets. By comparing causal networks learned from meteorological reanalyses (as a proxy for Earth observations) and CMIP climate model output, I demonstrate that climate models which better reproduce the observed causal information flow also better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe, and North America. The method is also able to identify expected model interdependencies. Finally, I find that causal network metrics provide stronger relationships for constraining precipitation projections under climate change than traditional model evaluation metrics. Such emergent relationships highlight the potential of causal discovery approaches to constrain longstanding uncertainties in climate change projections.

Time allowing, I will briefly discuss recent work by my group on constraining the uncertain role of clouds in global warming, machine learning parameterizations for ozone in Earth system models [3,4], low-cost air pollution sensor calibrations using machine learning [5], and a new blocking detection algorithm using self-organizing maps [6].

References:

[1] Nowack P, Runge J, Eyring V, Haigh JD. Causal networks for climate model evaluation and constrained projections. Nature Communications 11, 1415 (2020).

[2] Runge J, Nowack P, Kretschmer M, Flaxman S, Sejdinovic D. Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances 5, eaau4996 (2019).

[3] Nowack et al. Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations. Environmental Research Letters 13, 104016 (2018).

[4] Nowack et al. Machine learning parameterizations for ozone: climate model transferability. Proceedings of the 9th International Workshop on Climate Informatics 9, 263-268 (2019).

[5] Nowack et al. Towards low-cost and high-performance air pollution measurements using machine learning calibration techniques. Atmospheric Measurement Techniques Discussions (2021).

[6] Thomas et al. An unsupervised learning approach to identifying blocking events: the case of European summer. Weather and Climate Dynamics Discussions (2021).

Join Microsoft Teams Meeting

Learn more about Teams | Meeting options

By using this service you agree to LU T&Cs - Use the Help Button for tips

Help | Legal