Articles | Volume 8, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, 2022
https://doi.org/10.5194/ascmo-8-97-2022
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, 2022
https://doi.org/10.5194/ascmo-8-97-2022
 
16 May 2022
16 May 2022

Comparing climate time series – Part 3: Discriminant analysis

Timothy DelSole and Michael K. Tippett

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Cited articles

Abdi, H. and Williams, L. J.: Principal component analysis, WIREs Comput. Stat., 2, 433–459, https://doi.org/10.1002/wics.101, 2010. a, b
Alexander, M. A., Matrosova, L., Penland, C., Scott, J. D., and Chang, P.: Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO, J. Clim., 21, 385–402, https://doi.org/10.1175/2007JCLI1849.1, 2008. a
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CMIP5: CLIVAR Exchanges – Special Issue: WCRP Coupled Model Intercomparison Project – Phase 5 – CMIP5, Project Report 56, CMIP5 [data set], https://eprints.soton.ac.uk/194679/ (last access: 7 May 2020), 2011. a
Short summary
A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.