Articles | Volume 7, issue 2
https://doi.org/10.5194/ascmo-7-73-2021
https://doi.org/10.5194/ascmo-7-73-2021
02 Dec 2021
 | 02 Dec 2021

Comparing climate time series – Part 2: A multivariate test

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
Allen, M. R. and Tett, S. F. B.: Checking for model consistency in optimal fingerprinting, Clim. Dynam., 15, 419–434, 1999. a
Anderson, T. W.: An Introduction to Multivariate Statistical Analysis, Wiley-Interscience, USA, 1984. a, b
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y., Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Webbari, R., and Zhang, X.: Detection and Attribution of Climate Change: From Global to Regional, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., chap. 10, 867–952, Cambridge University Press, New York, 2013. a, b
Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T., and Bellouin, N.: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability, Nature, 484, 228–232, https://doi.org/10.1038/nature10946, 2012. a, b
Short summary
After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here, realistic does not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
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