Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 409–422, 2021
https://doi.org/10.5194/npg-28-409-2021
Nonlin. Processes Geophys., 28, 409–422, 2021
https://doi.org/10.5194/npg-28-409-2021
Research article
03 Sep 2021
Research article | 03 Sep 2021

The blessing of dimensionality for the analysis of climate data

Bo Christiansen

Related authors

Climatic signatures in early modern European grain harvest yields
Fredrik Charpentier Ljungqvist, Bo Christiansen, Jan Esper, Heli Huhtamaa, Lotta Leijonhufvud, Christian Pfister, Andrea Seim, Martin Karl Skoglund, and Peter Thejll
Clim. Past Discuss., https://doi.org/10.5194/cp-2022-88,https://doi.org/10.5194/cp-2022-88, 2022
Preprint under review for CP
Short summary
Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting
Torben Schmith, Peter Thejll, Peter Berg, Fredrik Boberg, Ole Bøssing Christensen, Bo Christiansen, Jens Hesselbjerg Christensen, Marianne Sloth Madsen, and Christian Steger
Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021,https://doi.org/10.5194/hess-25-273-2021, 2021
Short summary
Trends and annual cycles in soundings of Arctic tropospheric ozone
Bo Christiansen, Nis Jepsen, Rigel Kivi, Georg Hansen, Niels Larsen, and Ulrik Smith Korsholm
Atmos. Chem. Phys., 17, 9347–9364, https://doi.org/10.5194/acp-17-9347-2017,https://doi.org/10.5194/acp-17-9347-2017, 2017
Short summary

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315, https://doi.org/10.5194/npg-29-301-2022,https://doi.org/10.5194/npg-29-301-2022, 2022
Short summary
Predicting sea surface temperatures with coupled reservoir computers
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264, https://doi.org/10.5194/npg-29-255-2022,https://doi.org/10.5194/npg-29-255-2022, 2022
Short summary
Using neural networks to improve simulations in the gray zone
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181, https://doi.org/10.5194/npg-29-171-2022,https://doi.org/10.5194/npg-29-171-2022, 2022
Short summary
Producing realistic climate data with generative adversarial networks
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021,https://doi.org/10.5194/npg-28-347-2021, 2021
Short summary
Identification of droughts and heatwaves in Germany with regional climate networks
Gerd Schädler and Marcus Breil
Nonlin. Processes Geophys., 28, 231–245, https://doi.org/10.5194/npg-28-231-2021,https://doi.org/10.5194/npg-28-231-2021, 2021
Short summary

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, https://doi.org/10.5194/esd-10-91-2019, 2019. a
Annan, J. D. and Hargreaves, J. C.: Reliability of the CMIP3 ensemble, Geophys. Res. Lett., 37, L02703, https://doi.org/10.1029/2009GL041994, 2010. a
Bartlett, M. S.: Some aspects of the time-correlation problem in regard to tests of significance, J. R. Stat. Soc., 98, 536–543, https://doi.org/10.2307/2342284, 1935. a
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, https://doi.org/10.1007/s00382-018-4343-8, 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
Download
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.