The blessing of dimensionality for the analysis of climate data

Christiansen, Bo

doi:https://doi.org/10.5194/npg-28-409-2021

Articles | Volume 28, issue 3

https://doi.org/10.5194/npg-28-409-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-28-409-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 3

Research article

|

03 Sep 2021

Research article |

| 03 Sep 2021

The blessing of dimensionality for the analysis of climate data

Bo Christiansen

Data sets

Daily surface meteorological data set for agronomic use, based on ERA5 ECMWF https://doi.org/10.24381/cds.6c68c9bb

Coupled Model Intercomparison Project – Phase 5, World Climate Research Programme (WCRP) ESGF https://esgf-node.llnl.gov/projects/esgf-llnl/

The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system variability (https://www.mpimet.mpg.de/en/grand-ensemble/) Nicola Maher, Sebastian Milinski, Laura Suarez-Gutierrez, Michael Botzet, Mikhail Dobrynin, Luis Kornblueh, Jürgen Kröger, Yohei Takano, Rohit Ghosh, Christopher Hedemann, Chao Li, Hongmei Li, Elisa Manzini, Dirk Notz, Dian Putrasahan, Lena Boysen, Martin Claussen, Tatiana Ilyina, Dirk Olonscheck, Thomas Raddatz, Bjorn Stevens, and Jochem Marotzke https://doi.org/10.1029/2019MS001639

Short summary

In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.