Articles | Volume 8, issue 2
https://doi.org/10.5194/ascmo-8-225-2022
https://doi.org/10.5194/ascmo-8-225-2022
14 Dec 2022
 | 14 Dec 2022

Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory

Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg

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Cited articles

Allen, M. R. and Stott, P. A.: Estimating signal amplitudes in optimal fingerprinting, part I: theory, Clim. Dynam., 21, 477–491, https://doi.org/10.1007/s00382-003-0313-9, 2003. a, b, c, d, e
Bollen, K. A.: Structural equations with latent variables, Wiley, ISBN 0471011711, 1989. a, b, c, d
Boomsma, A.: Reporting Analyses of Covariance Structures, Struct. Equ. Modeling, 7, 461–483, https://doi.org/10.1207/S15328007SEM0703_6, 2000. a
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., and Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res., 111, D12106, https://doi.org/10.1029/2005JD006548, 2006. a
Cheng, C.-L. and van Ness, J. W.: Statistical regression with measurement error, Kendall's Library of Statistics, Oxford University Press Inc., New York, ISBN 0340614617, 1999. a
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This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
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