Journal metrics

Journal metrics

  • IF value: 1.129 IF 1.129
  • IF 5-year value: 1.519 IF 5-year 1.519
  • CiteScore value: 1.54 CiteScore 1.54
  • SNIP value: 0.798 SNIP 0.798
  • SJR value: 0.610 SJR 0.610
  • IPP value: 1.41 IPP 1.41
  • h5-index value: 21 h5-index 21
  • Scimago H index value: 48 Scimago H index 48
Nonlin. Processes Geophys., 19, 95-111, 2012
https://doi.org/10.5194/npg-19-95-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
13 Feb 2012
Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability
R. V. Donner1, R. Ehrcke1, S. M. Barbosa2, J. Wagner1, J. F. Donges1,3, and J. Kurths1,3,4 1Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam, Germany
2Instituto Dom Luiz, University of Lisbon, Campo Grande, Edifício C8, 1749-016 Lisboa, Portugal
3Department of Physics, Humboldt University Berlin, Newtonstr. 15, 12489 Berlin, Germany
4Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB243UE, UK
Abstract. The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.

Citation: Donner, R. V., Ehrcke, R., Barbosa, S. M., Wagner, J., Donges, J. F., and Kurths, J.: Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability, Nonlin. Processes Geophys., 19, 95-111, https://doi.org/10.5194/npg-19-95-2012, 2012.
Publications Copernicus
Download
Share