Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 10, issue 1
Articles | Volume 10, issue 1
https://doi.org/10.5194/ascmo-10-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-10-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 1
16 Jan 2024
 | 16 Jan 2024

Comparison of climate time series – Part 5: Multivariate annual cycles

Timothy DelSole and Michael K. Tippett

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Cited articles

Alexander, M. A., Matrosova, L., Penland, C., Scott, J. D., and Chang, P.: Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO, J. Climate, 21, 385–402, https://doi.org/10.1175/2007JCLI1849.1, 2008. a, b
Anderson, T. W.: An Introduction to Multivariate Statistical Analysis, Wiley-Interscience, ISBN 978-0-471-36091-9, 1984. a, b
Bach, E., Motesharrei, S., Kalnay, E., and Ruiz-Barradas, A.: Local Atmosphere–Ocean Predictability: Dynamical Origins, Lead Times, and Seasonality, J. Climate, 32, 7507–7519, https://doi.org/10.1175/JCLI-D-18-0817.1, 2019. a
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C.: Time Series Analysis: Forecasting and Control, Wiley-Interscience, 4th edn., ISBN 978-1-118-67502-1, 2008. a, b, c
Brockwell, P. J. and Davis, R. A.: Time Series: Theory and Methods, Springer Verlag, 2nd edn., ISBN 0-387-97482-2, 1991. a, b
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This paper introduces a method to assess whether two data sets come from the same source. Current methods do not adequately consider spatial and temporal correlations and their annual cycles in a comprehensive test. This method addresses that gap, thereby providing a new and rigorous tool for evaluating the realism of climate simulations and measuring changes in variability over time.
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