Abstract
This study applies methods from causal discovery
theory to the output data of climate models.
Causal discovery seeks to identify potential cause-effect
relationships from data and is used here to learn so-called
causal signatures from the data that indicate interactions
between the different atmospheric variables. We hope
that these causal signatures can act like finger prints for
the underlying dynamics, and can as such be used in a
variety of applications. Sample applications include (1)
distinguishing correct model runs from incorrect ones, i.e.
providing an additional error check for climate model runs
and (2) assessing the impact of data compression on the
causal signatures, as a means to determine which type and
amount of compression is acceptable. Still being in the
early stages of this project, we primarily describe work in
progress and future work.
theory to the output data of climate models.
Causal discovery seeks to identify potential cause-effect
relationships from data and is used here to learn so-called
causal signatures from the data that indicate interactions
between the different atmospheric variables. We hope
that these causal signatures can act like finger prints for
the underlying dynamics, and can as such be used in a
variety of applications. Sample applications include (1)
distinguishing correct model runs from incorrect ones, i.e.
providing an additional error check for climate model runs
and (2) assessing the impact of data compression on the
causal signatures, as a means to determine which type and
amount of compression is acceptable. Still being in the
early stages of this project, we primarily describe work in
progress and future work.
| Original language | American English |
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| Title of host publication | What can we learn about climate model runs from their causal signatures |
| Number of pages | 2 |
| State | Published - 2015 |
Publication series
| Name | Proceedings of the Fifth International Workshop on Climate Informatics: CI 2015 |
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