Original paper

Linking teleconnection patterns to European temperature – a multiple linear regression model

Rust, Henning W.; Richling, Andy; Bissolli, Peter; Ulbrich, Uwe

Meteorologische Zeitschrift Vol. 24 No. 4 (2015), p. 411 - 423

37 references

published: Jul 21, 2015
published online: Apr 4, 2015
manuscript accepted: Jan 26, 2015
manuscript revision received: Dec 23, 2014
manuscript revision requested: Nov 11, 2014
manuscript received: Aug 4, 2014

DOI: 10.1127/metz/2015/0642

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The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month. Furthermore index-specific signals are calculated to estimate the contribution to temperature anomalies caused by each individual teleconnection pattern. To this extent, an observational product of monthly mean temperature (E-OBS), as well as monthly time series of teleconnection indices (CPC, NOAA) for the period 1951–2010 are evaluated. The stepwise regression approach is used to build grid cell based models for each month on the basis of the five most important teleconnection indices (NAO, EA, EAWR, SCAND, POLEUR), which are motivated by an exploratory correlation analysis. The temperature links are dominated by NAO and EA in Northern, Western, Central and South Western Europe, by EAWR during summer/autumn in Russia/Fenno-Scandia and by SCAND in Russia/Northern Europe; POLEUR shows minor effects only. In comparison to the climatological forecast, the presented linear regression models improve the temperature modelling by 30–40 % with better results in winter and spring. They can be used to model the spatial distribution and structure of observed temperature anomalies, where two to three patterns are the main contributors. As an example the estimated temperature signals induced by the teleconnection indices is shown for February 2010.


Teleconnection PatternNAOEuropean TemperatureLinear ModelStepwise Regression