Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 409–422, 2021
Nonlin. Processes Geophys., 28, 409–422, 2021

Research article 03 Sep 2021

Research article | 03 Sep 2021

The blessing of dimensionality for the analysis of climate data

Bo Christiansen

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Cited articles

Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105,, 2019. a
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Bengtsson, L. and Hodges, K. I.: Can an ensemble climate simulation be used to separate climate change signals from internal unforced variability?, Clim. Dynam., 52, 3553–3573,, 2019. a
Bishop, C.: Pattern recognition and machine learning (Information science and statistics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2nd edn., 2007. a
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.