The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2009 vol.15)
pp: 1375-1382
Heike Jänicke , University of Leipzig
Michael Böttinger , German Climate Computing Center (DKRZ)
Uwe Mikolajewicz , Max Planck Institute for Meteorology (MPI-M)
Gerik Scheuermann , University of Leipzig
Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Ni˜no Southern Oscillation phenomenon. Beside a shift of the mean states and of extreme values of climate variables, climate change may also change the frequency or the spatial patterns of these natural climate variations. Wavelet analysis is a well established tool to investigate variability in the frequency domain. However, due to the size and complexity of the analysis results, only few time series are commonly analyzed concurrently. In this paper we will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years. Our new framework and data from the IPCC AR4 simulations with the coupled climate model ECHAM5/MPI-OM are used to explore the temporal evolution of El Ni˜no due to climate change.
Wavelet analysis, multivariate data, time-dependent data, climate variability change visualization, El Nino
Heike Jänicke, Michael Böttinger, Uwe Mikolajewicz, Gerik Scheuermann, "Visual Exploration of Climate Variability Changes Using Wavelet Analysis", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 6, pp. 1375-1382, November/December 2009, doi:10.1109/TVCG.2009.197
[1] G. E. P. Box and G. Jenkins, Time Series Analysis: Forecasting and Control. Holden-Day, 1976.
[2] T. M. Cover and J. A. Thomas, Elements of information theory. Wiley-Interscience, New York, NY, USA, 1991.
[3] H. Doleisch, P. Muigg, and H. Hauser, Interactive visual analysis of hurricane isabel with simvis. In IEEE Visualization (Vis'04) Contest, 2004.
[4] M. Farge, Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics, 24: 295–457, 1992.
[5] M. Frigo and S. G. Johnson, The design and implementation of FFTW3. Proceedings of the IEEE, 93 (2): 216–231, 2005.
[6] M. Gross, O. Staadt, and R. Gatti, Efficient triangular surface approximations using wavelets and quadtree data structures. Visualization and Computer Graphics, IEEE Transactions on, 2 (2): 130–143, June 1996.
[7] B. Jähne, Digital Image Processing - Concepts, Algorithmus and Scientific Applications. Springer-Verlag, Heidelberg, 1991.
[8] H. Jänicke, M. Böttinger, and G. Scheuermann, Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1459–1466, 2008.
[9] G. M. Jenkins and D. G. Watts, Spectral Analysis and Its Applications. Holden-Day, 1968.
[10] K. Lau and H. Weng, Climate signal detection using wavelet transform: How to make a time series sing. Bull. Am. Met. S., 76: 2391–2402, 1995.
[11] K.-L. Ma and H.-W. Shen, Vis. Handbook, chapter Visualization Techniques for Time-Varying Volume Data. Academic Press, Inc., 2004.
[12] D. Middleton, T. Scheitlin, and B. Wilhelmson, The Vis. Handbook, chapter Visualization in Weather and Climate Research. Elsevier, 2005.
[13] L. B. Mohr, Understanding significance testing. Sage Pub., Inc., 2003.
[14] C. M. Moy, G. O. Seltzer, D. T. Rodbell, and D. M. Anderson, Variability of el nino/southern oscillation activity at millennial timescales during the holocene epoch. Nature, 420: 162–165, 2002.
[15] W. A. Müller and E. Roeckner, ENSO teleconnections in projections of future climate in ECHAM5/MPI-OM. Clim. Dyn., 31: 533–549, 2008.
[16] G. M. Nielson, I.-H. Jung, and J. Sung, Haar wavelets over triangular domains with applications to multiresolution models for flow over a sphere. In IEEE Visualization, pages 143–150, 1997.
[17] T. Nocke, S. Schlechtweg, and H. Schumann, Icon-based visualization using mosaic metaphors. In Ninth International Conference on Information Visualisation (IV'05), pages 103–109, 2005.
[18] T. Nocke, T. Sterzel, M. Böttinger, and M. Wrobel, Visualization of climate and climate change data: An overview. In Ehlers et al. (Eds.) Digital Earth Summit on Geoinformatics 2008: Tools for Global Change Research (ISDE'08), Wichmann, Heidelberg, pages 226–232, 2008.
[19] D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2000.
[20] P. Piscaronoft, J. Kalvová, and R. Brázdil, Cycles and trends in the czech temperature series using wavelet transforms. Intern. Journal of Climatology, 24 (13): 1661–1670, 2004.
[21] R. W. Preisendorfer, Principal Component Analysis in Meteorology and Oceanography (Develop. in Atmos. Sci.), volume 17. Elsevier, 1988.
[22] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C. Cambridge University Press, 1988.
[23] W. Ribarsky, N. Faust, Z. Wartell, C. Shaw, and J. Jang, R. Ladner, K. Shaw, and Mahdi Abdelguerfi, Editors: Mining Spatio-Temporal Information Systems, chapter Visual Query of Time-Dependent 3D Weather in a Global Geospatial Environment. Kluwer, 2002.
[24] K. Riley, D. Ebert, C. Hansen, and J. Levit, Visually accurate multi-field weather visualization. In 14th IEEE Visualization 2003 (VIS 2003), 2003.
[25] E. Roeckner et al. The atmospheric general circulation model ECHAM 5. PART I: Model description. Tech. Rep. 349, Max Planck Inst. for Meteorol., Hamburg, Germany, 2003.
[26] A. Simmons, J. Wallace, and G. Branstator, Barotropic wave propagation and instability, and atmospheric teleconnection patterns. J. Atmos Sci., 40: 1363–1392, 1983.
[27] D. M. Sonechkin and N. M. Datsenko, Wavelet Analysis of Nonstationary and Chaotic Time Series with an Application to the Climate Change Problem. Pure and Applied Geophysics, 157: 653–677, 2000.
[28] R. Stöckli, E. Vermote, N. Saleos, R. Simmon, and D. Herring, The blue marble next generation - a true color earth dataset including seasonal dynamics from modis. Sumitted to EOS (AGU), 2005.
[29] A. Strehl and J. Ghosh, Cluster ensembles − a knowledge reuse framework for combining partitionings. In Proc. Conference on Artificial Intelligence (AAAI 2002), Edmonton, pages 93–98. AAAI/MIT Press, 2002.
[30] A. Timmermann, J. Oberhuber, A. Bacher, M. Esch, M. Latif, and E. Roeckner, Increased el niño frequency in a climate model forced by future greenhouse warming. Nature, 398: 694–697, 1999.
[31] C. Torrence and G. P. Compo, A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79: 61–78, 1998.
[32] K. E. Trenberth, El Niño Definition. Exchanges, Newsletter of the Clim. Variability and Predictability Programme (CLIVAR), 1 (3): 6–8, 1996.
[33] Working Group I contribution to the Fourth Assessment Report of the IPCC. IPCC AR4: Climate Change 2007 - The Physical Science Basis. Technical report, IPCC, 2007.
15 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool