Granger causal inference for climate change attribution
Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify...
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Climate |
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| Online Access: | https://doi.org/10.1088/2752-5295/add046 |
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| author | Mark D Risser Mohammed Ombadi Michael F Wehner |
| author_facet | Mark D Risser Mohammed Ombadi Michael F Wehner |
| author_sort | Mark D Risser |
| collection | DOAJ |
| description | Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality. |
| format | Article |
| id | doaj-art-44ba178db0ac4f79998f9be1e11fe635 |
| institution | OA Journals |
| issn | 2752-5295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | Environmental Research: Climate |
| spelling | doaj-art-44ba178db0ac4f79998f9be1e11fe6352025-08-20T01:51:23ZengIOP PublishingEnvironmental Research: Climate2752-52952025-01-014202200110.1088/2752-5295/add046Granger causal inference for climate change attributionMark D Risser0https://orcid.org/0000-0003-1956-1783Mohammed Ombadi1Michael F Wehner2https://orcid.org/0000-0001-5991-0082Climate and Ecosystem Sciences Division, LBNL , Berkeley, CA 94720, United States of AmericaCollege of Engineering, University of Michigan , Ann Arbor, MI 48109, United States of AmericaApplied Mathematics and Computational Research Division, LBNL , Berkeley, CA 94720, United States of AmericaClimate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.https://doi.org/10.1088/2752-5295/add046causal inferencepearl causalitystatistical counterfactualdetection and attribution |
| spellingShingle | Mark D Risser Mohammed Ombadi Michael F Wehner Granger causal inference for climate change attribution Environmental Research: Climate causal inference pearl causality statistical counterfactual detection and attribution |
| title | Granger causal inference for climate change attribution |
| title_full | Granger causal inference for climate change attribution |
| title_fullStr | Granger causal inference for climate change attribution |
| title_full_unstemmed | Granger causal inference for climate change attribution |
| title_short | Granger causal inference for climate change attribution |
| title_sort | granger causal inference for climate change attribution |
| topic | causal inference pearl causality statistical counterfactual detection and attribution |
| url | https://doi.org/10.1088/2752-5295/add046 |
| work_keys_str_mv | AT markdrisser grangercausalinferenceforclimatechangeattribution AT mohammedombadi grangercausalinferenceforclimatechangeattribution AT michaelfwehner grangercausalinferenceforclimatechangeattribution |