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On smoothing potentially non‐stationary climate time series
Abstract
A simple approach to the smoothing of a potentially non‐stationary time series is presented which provides an optimal choice among three alternative, readily motivated and easily implemented boundary constraints. This method is applied to the smoothing of the instrumental Northern Hemisphere (NH) annual mean and cold‐season North Atlantic Oscillation (NAO) time series, yielding an objective estimate of the smoothed decadal‐scale variations in these series including long‐term trends.
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Citations
Cited by
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Knowles, N., Dettinger, M. D., & Cayan, D. R. (2006) · Journal of Climate · doi:10.1175/jcli3850.1 · Cited by 934
Meinshausen, M., Raper, S. C. B., & Wigley, T. M. L. (2011) · Atmospheric Chemistry and Physics · doi:10.5194/acp-11-1417-2011 · Cited by 876
Modelling Palaeoecological Time Series Using Generalised Additive Models
Simpson, G. L. (2018) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2018.00149 · Cited by 560
Nonlinear trends and multiyear cycles in sea level records
Jevrejeva, S., Grinsted, A., Moore, J. C., & Holgate, S. (2006) · Journal of Geophysical Research: Oceans · doi:10.1029/2005jc003229 · Cited by 373
Sahel rainfall and decadal to multi-decadal sea surface temperature variability
Mohino, E., Janicot, S., & Bader, J. (2011) · Climate Dynamics · doi:10.1007/s00382-010-0867-2 · Cited by 306
Metadata
- Authors
- Mann, M. E.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2004-04-15
- Volume
- 31
- Issue
- 7
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2160336235
- Cited by
- 158
- References
- 0
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mann, M. E. (2004). On smoothing potentially non‐stationary climate time series. Geophysical Research Letters. https://doi.org/10.1029/2004gl019569
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
