Causal networks for climate model evaluation and constrained projections

Nowack, Peer, Runge, Jakob, Eyring, Veronika and Haigh, Joanna D. (2020) Causal networks for climate model evaluation and constrained projections. Nature Communications, 11 (1). ISSN 2041-1723

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Abstract

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 we 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. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections.

Item Type: Article
Additional Information: doi: 10.1038/s41467-020-15195-y
Uncontrolled Keywords: climate models,earth observations,atmospheric dynamics,causal discovery,network algorithms,model evaluation and intercomparison,machine learning,cmip5,climate change,precipitation patterns,ensemble,circulation,performance,metrics,feedbacks,impact,enso teleconnections,uncertainty,atmospheric teleconnections,physics and astronomy(all),chemistry(all),biochemistry, genetics and molecular biology(all) ,/dk/atira/pure/subjectarea/asjc/3100
Faculty \ School: Faculty of Science > School of Environmental Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 18 Mar 2020 07:16
Last Modified: 30 Sep 2020 23:54
URI: https://ueaeprints.uea.ac.uk/id/eprint/74523
DOI: 10.1038/s41467-020-15195-y

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