Causal networks for climate model evaluation and constrained projections
| Autor: | Veronika Eyring, Peer Nowack, Joanna D. Haigh, Jakob Runge |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
010504 meteorology & atmospheric sciences Computer science Datenmanagement und Analyse IMPACT General Physics and Astronomy UNCERTAINTY causal networks ATMOSPHERIC TELECONNECTIONS Metrics 01 natural sciences Proxy (climate) Machine Learning constrained projections lcsh:Science media_common Multidisciplinary Environmental resource management climate model evaluation METRICS Causality Multidisciplinary Sciences General Circulation Model FEEDBACKS Science & Technology - Other Topics media_common.quotation_subject Science Climate Change CIRCULATION Climate change ENSEMBLE General Biochemistry Genetics and Molecular Biology Article Projection and prediction 03 medical and health sciences Atmospheric science East Asia CMIP5 Erdsystemmodell -Evaluation und -Analyse Climate and Earth system modelling 0105 earth and related environmental sciences Science & Technology business.industry Atmosphere Storm General Chemistry PERFORMANCE Interdependence 030104 developmental biology Climate model lcsh:Q business ENSO TELECONNECTIONS Climate sciences |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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