Applications of matrix factorization methods to climate data

Harries, Dylan; O'Kane, Terence J.

doi:https://doi.org/10.5194/npg-27-453-2020

Articles | Volume 27, issue 3

https://doi.org/10.5194/npg-27-453-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-27-453-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 3

Research article

|

17 Sep 2020

Research article |

| 17 Sep 2020

Applications of matrix factorization methods to climate data

Dylan Harries and Terence J. O'Kane

Data sets

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century (https://www.metoffice.gov.uk/hadobs/hadisst/) N. A. Rayner, D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan https://doi.org/10.1029/2002JD002670

The JRA-55 Reanalysis: General Specifications and Basic Characteristics (https://jra.kishou.go.jp/JRA-55/index_en.html) S. Kobayashi, Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi https://doi.org/10.2151/jmsj.2015-001

Model code and software

Matrix factorization case studies code D. Harries and T. J. O'Kane https://doi.org/10.5281/zenodo.3723948

Short summary

Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.