Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4307-2021
https://doi.org/10.5194/gmd-14-4307-2021
Methods for assessment of models
 | 
08 Jul 2021
Methods for assessment of models |  | 08 Jul 2021

Multi-variate factorisation of numerical simulations

Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes

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Cited articles

Alpert, P. and Sholokhman, T. (Eds.): Factor Separation in the Atmosphere: Applications and Future Prospects, Cambridge University Press, https://doi.org/10.1017/CBO9780511921414, 2011. a
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Burke, K. D., Williams, J. W., Chandler, M. A., Haywood, A. M., Lunt, D. J., and Otto-Bliesner, B. L.: Pliocene and Eocene provide best analogs for near-future climates, P. Natl. Acad. Sci. USA, 115, 13288–13293, https://doi.org/10.1073/pnas.1809600115, 2018. a
Chandan, D.: Pliocene surface temperature data for Multi-variate factorisation methods, Scholars Portal Dataverse, V1, https://doi.org/10.5683/SP2/QGK5B0, 2020. a
Chandan, D. and Peltier, W. R.: Regional and global climate for the mid-Pliocene using the University of Toronto version of CCSM4 and PlioMIP2 boundary conditions, Clim. Past, 13, 919–942, https://doi.org/10.5194/cp-13-919-2017, 2017. a
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Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by swapping in and out different values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.