Articles | Volume 5, issue 2
https://doi.org/10.5194/ascmo-5-161-2019
https://doi.org/10.5194/ascmo-5-161-2019
26 Nov 2019
 | 26 Nov 2019

An improved projection of climate observations for detection and attribution

Alexis Hannart

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Latest update: 19 Jun 2024
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Short summary
In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the signal is the anthropogenic response and the noise is climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.