The DOI matches its Crossref record; 13 of the 15 references it cites were found in a registry or an index.
Application of the cross wavelet transform and wavelet coherence to geophysical time series
Open accessAbstract
Abstract. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).
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Cites
Baltosser, W. H., & Zar, J. H. (1996) · Ecology · doi:10.2307/2265725 · Cited by 35,166
The Arctic oscillation signature in the wintertime geopotential height and temperature fields
Thompson, D. W. J., & Wallace, J. M. (1998) · Geophysical Research Letters · doi:10.1029/98gl00950 · Cited by 4,158
The quasi‐biennial oscillation
Baldwin, M., Gray, L. J., Dunkerton, T. J., Hamilton, K., Haynes, P., Randel, W. J., Holton, J. R., Alexander, M. J., Hirota, I., Horinouchi, T., et al. (2001) · Reviews of Geophysics · doi:10.1029/1999rg000073 · Cited by 2,119
Nobre, P., & Srukla, J. (1996) · Journal of Climate · doi:10.1175/1520-0442(1996)009 · Cited by 893
Jevrejeva, S., Moore, J. C., & Grinsted, A. (2003) · Journal of Geophysical Research Atmospheres · doi:10.1029/2003jd003417 · Cited by 427
Self-citation(1994) · Wavelet analysis and its applications · doi:10.1016/c2009-0-21241-4 · Cited by 376
Influence of atmospheric circulation on the maximum ice extent in the Baltic Sea
Omstedt, A., & Chen, D. (2001) · Journal of Geophysical Research Atmospheres · doi:10.1029/1999jc000173 · Cited by 139
Jevrejeva, S., & Moore, J. C. (2001) · Geophysical Research Letters · doi:10.1029/2001gl013573 · Cited by 55
Self-citationLoewe, P., & Koslowski, G. (1998) · Tellus A Dynamic Meteorology and Oceanography · doi:10.3402/tellusa.v50i2.14522 · Cited by 28
Loewe, P., & Koslowski, G. (1998) · Tellus A Dynamic Meteorology and Oceanography · doi:10.1034/j.1600-0870.1998.t01-1-00005.x · Cited by 21
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Metadata
- Authors
- Grinsted, A., Moore, J. C., & Jevrejeva, S.
- Affiliations
- University of Lapland (FI)
- Venue
- Nonlinear Processes in Geophysics
- Publisher
- Copernicus GmbH
- Published
- 2004-11-18
- Volume
- 11
- Issue
- 5/6
- Pages
- 561-566
- ISSN
- 1023-5809 (ISSN-L)
- 1607-7946
- OpenAlex
- W2124459709
- Cited by
- 6,285
- References
- 15
- Citations by year
- 2026: 468 · 2025: 580 · 2024: 517 · 2023: 494 · 2022: 551 · 2021: 547 · 2020: 468 · 2019: 417 · 2018: 373 · 2017: 313 · 2016: 323 · 2015: 229 · 2014: 201 · 2013: 177 · 2012: 156
- FWCI
- 29.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nonlinear processes in geophysics (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- LaCRIS (University of Lapland) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Keywords
- cross-wavelet transform, wavelet coherence, geophysical time series, time-frequency analysis, Monte Carlo significance testing
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