An improved projection of climate observations for detection and attribution
<p>An important goal of climate research is to determine the causal contribution of human activity to observed changes in the climate system. Methodologically speaking, most climatic causal studies to date have been formulating attribution as a linear regression inference problem. Under this f...
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-11-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://www.adv-stat-clim-meteorol-oceanogr.net/5/161/2019/ascmo-5-161-2019.pdf |
| Summary: | <p>An important goal
of climate research is to determine the causal contribution of human activity
to observed changes in the climate system. Methodologically speaking, most
climatic causal studies to date have been formulating attribution as a linear
regression inference problem. Under this formulation, the inference is often
obtained by using the generalized least squares (GLS) estimator after
projecting the data on the <span class="inline-formula"><i>r</i></span> leading eigenvectors of the covariance
associated with internal variability, which are evaluated from numerical
climate models. In this paper, we revisit the problem of obtaining a GLS
estimator adapted to this particular situation, in which only the leading
eigenvectors of the noise's covariance are assumed to be known. After noting
that the eigenvectors associated with the lowest eigenvalues are in general
more valuable for inference purposes, we introduce an alternative estimator.
Our proposed estimator is shown to outperform the conventional estimator,
when using a simulation test bed that represents the 20th century temperature
evolution.</p> |
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| ISSN: | 2364-3579 2364-3587 |