Abstract
We employed selected methods of time series analysis to investigate the spatial and seasonal variations of nonlinearity in the NCEP/NCAR reanalysis data and in the outputs of the global climate model HadCM3 of the Hadley Center. The applied nonlinearity detection techniques were based on a direct comparison of the results of prediction by multiple linear regression and by the method of local linear models, complemented by tests using surrogate data. Series of daily values of relative topography and geopotential height were analyzed. Although some differences of the detected patterns of nonlinearity were found, their basic features seem to be identical for both the reanalysis and the model outputs. Most prominently, the distinct contrast between weak nonlinearity in the equatorial area and stronger nonlinearity in higher latitudes was well reproduced by the HadCM3 model. Nonlinearity tends to be slightly stronger in the model outputs than in the reanalysis data. Nonlinear behavior was generally stronger in the colder part of the year in the mid-latitudes of both hemispheres, for both analyzed datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
M. Paluš, D. Novotná: Testing for nonlinearity in weather records, Phys. Lett. A 193, 67 (1994)
D. A. S. Patil, B. R. Hunt, J. A. Carton: Identifying low-dimensional nonlinear behavior in atmospheric data, Mon. Weather Rev. 129, 2116 (2001)
J. Miksovsky, A. Raidl: Testing for nonlinearity in European climatic time series by the method of surrogate data, Theor. Appl. Climatol. 83, 21 (2006)
M. C. Casdagli: Characterizing Nonlinearity in Weather and Epilepsy Data: A Personal View. In:Nonlinear Dynamics and Time Series, ed by C. D. Cutler, D. T. Kaplan (American Mathematical Society, Providence, Rhode Island 1997) pp 201–222
G. Sugihara, M. Casdagli, E. Habjan, et al.: Residual delay maps unveil global patterns of atmospheric nonlinearity and produce improved local forecasts, P. Natl. Acad. Sci. USA 96, 14210 (1999)
J. Miksovsky, A. Raidl: Testing the performance of three nonlinear methods of time series analysis for prediction and downscaling of European daily temperatures, Nonlinear Proc. Geoph. 12, 979 (2005)
M. Paluš: Detecting nonlinearity in multivariate time series, Phys. Lett. A 213, 138 (1996)
A. A. Tsonis: Probing the linearity and nonlinearity in the transitions of the atmospheric circulation, Nonlinear Proc. Geoph. 8, 341 (2001)
T. Schreiber, A. Schmitz: Surrogate time series, Physica D 142, 346 (2000)
I. Bartos, I. M. Jánosi: Nonlinear correlations of daily temperature records over land, Nonlinear Proc. Geoph. 13, 571 (2006)
V. Pérez-Muñuzuri, I. R. Gelpi: Application of nonlinear forecasting techniques for meteorological modeling, Ann. Geophysicae 18, 1349 (2000)
B. Tang, W. W. Hsieh, A. H. Monahan, F. T. Tangang: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures, J. Climate 13, 287 (2000)
A. Weichert, G. Bürger: Linear versus nonlinear techniques in downscaling, Climate Res. 10, 83 (1998)
J. T. Schoof, S. C. Pryor: Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks, Int. J. Climatol. 21, 773 (2001)
M. Casaioli, R. Mantovani, F. P. Scorzoni, et al.: Linear and nonlinear post-processing of numerically forecasted surface temperature, Nonlinear Proc. Geoph. 10, 373 (2003)
E. Eccel, L. Ghielmi, P. Granitto, et al.: Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models, Nonlinear Proc. Geoph. 14, 211 (2007)
W. von Bloh, M. C. Romano, M. Thiel: Long-term predictability of mean daily temperature data, Nonlinear Proc. Geoph. 12, 471 (2005)
E. Kalnay, M. Kanamitsu, R. Kistler, et al.: The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc. 77, 437 (1996)
R. Kistler, E. Kalnay, W. Collins, et al.: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation, Bull. Amer. Meteor. Soc. 82, 247 (2001)
C. Gordon, C. Cooper, C. A. Senior, et al.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam. 16, 147 (2000)
T. C. Johns, J. M. Gregory, W. J. Ingram, et al.: Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios, Clim. Dynam. 20, 583 (2003)
E. Ott, T. Sauer, J. A. Yorke (eds.): Coping with Chaos: Analysis of Chaotic Data and The Exploitation of Chaotic Systems(John Wiley & Sons, New York 1994)
H. Kantz, T. Schreiber: Nonlinear Time Series Analysis(Cambridge University Press, cambridge 1997)
D. S. Wilks: Statistical Methods in the Atmospheric Sciences, 2nd edn (Elsevier, Amsterdam 2006)
V. Venema, S. Bachner, H.W. Rust, C. Simmer: Statistical characteristics of surrogate data based on geophysical measurements, Nonlinear Proc. Geoph. 13, 449 (2006)
T. Schreiber, A. Schmitz: Improved surrogate data for nonlinearity tests, Phys. Rev. Lett. 77, 635 (1996)
R. Hegger, H. Kantz, T. Schreiber: Practical implementation of nonlinear time series methods: The TISEAN package, CHAOS 9, 413 (1999)
S. M. Uppala, P. W. Kållberg, A. J. Simmons, et al.: The ERA-40 re-analysis, Quart. J. R. Meteorol. Soc. 131, 2961 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mikšovský, J., Pišoft, P., Raidl, A. (2008). Global Patterns of Nonlinearity in Real and GCM-Simulated Atmospheric Data. In: Donner, R.V., Barbosa, S.M. (eds) Nonlinear Time Series Analysis in the Geosciences. Lecture Notes in Earth Sciences, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78938-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-78938-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78937-6
Online ISBN: 978-3-540-78938-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)