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

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
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
Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
Jonathan M. Lilly and Paula Pérez-Brunius
Nonlin. Processes Geophys., 28, 181–212, https://doi.org/10.5194/npg-28-181-2021,https://doi.org/10.5194/npg-28-181-2021, 2021
Short summary
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021,https://doi.org/10.5194/npg-28-61-2021, 2021
Short summary
Applications of matrix factorization methods to climate data
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471, https://doi.org/10.5194/npg-27-453-2020,https://doi.org/10.5194/npg-27-453-2020, 2020
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.