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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/npg-2016-29
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-2016-29
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2016

30 May 2016

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This preprint was under review for the journal NPG but the revision was not accepted.

Trend analysis by a piecewise linear regression model applied to surface air temperatures in Southeastern Spain (1973–2014)

Pablo Campra1 and Maria Morales2 Pablo Campra and Maria Morales
  • 1Engineering School D2.36, University of Almeria, Almeria 04120, Spain
  • 2Mathematics Department. University of Almeria, Almeria 04120, Spain

Abstract. The magnitude of the trends of environmental and climatic changes is mostly derived from the slopes of the linear trends using ordinary least-square fitting. An alternative flexible fitting model, piecewise regression, has been applied here to surface air temperature records in southeastern Spain for the recent warming period (1973–2014) to gain accuracy in the description of the inner structure of change, dividing the time series into linear segments with different slopes. Breakpoint years, with confidence intervals (CIs), were estimated and separated periods of significant trend change were determined. First, simple linear trends for mean, maximum and minimum surface air temperatures and diurnal temperature range (DTR) from the four longest and most reliable historic records in SE Spain were estimated. All series in the region showed intense linear warming signs during the period 1973–2014. However, updated warming trends were lower than those previously cited for the region and Spain from the 1970s onwards. Piecewise regression model allowed us to detect breakpoints in the series, and the absence of significant trends in the most recent period of the segmented fits for two stations. In general, piecewise regression model showed better fit than simple linear regression model, and thus, showed a better description of temperature variability.

Pablo Campra and Maria Morales

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Pablo Campra and Maria Morales

Pablo Campra and Maria Morales

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Short summary
Simple linear trend analysis constitutes the most straightforward assessment of the long-term behavior of time series in climate change. Here we have applied an alternative nonlinear fitting model of flexible regression developed to characterize climatic trends in surface air temperature series in SE Spain, a key region to study impacts of climate change. This model offers a better fit to the observational records than conventional simple linear trends analyses.
Simple linear trend analysis constitutes the most straightforward assessment of the long-term...
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