Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 8, issue 1
Articles | Volume 8, issue 1
https://doi.org/10.5194/ascmo-8-97-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-8-97-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 1
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

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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
Anderson, T. W.: An Introduction to Multivariate Statistical Analysis, Wiley-Interscience, 675 pp., 1984. a, b
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C.: Time Series Analysis: Forecasting and Control, Wiley-Interscience, 4th Edn., 746 pp., 2008. a
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
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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.
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