Skip to main content

Daily temperature grids for Austria since 1961—concept, creation and applicability

Abstract

Current interest into past climate change and its potential role for changes in the environment call for spatially distributed climate datasets of high temporal resolution and extending over several decades. To foster such research, we present a new gridded dataset of daily minimum and maximum temperature covering Austria at 1-km resolution and extending back till 1961 at daily time resolution. To account for the complex and highly variable thermal distributions in this high-mountain region, we adapt and employ a recently published interpolation method that estimates nonlinear temperature profiles with altitude and accounts for the non-Euclidean spatial representativity of station measurements. The spatial analysis builds upon 150 station series in and around Austria (homogenised where available), all of which extend over or were gap-filled to cover the entire study period. The restriction to (almost) complete records shall avoid long-term inconsistencies from changes in the station network. Systematic leave-one-out cross-validation reveals interpolation errors (mean absolute error) of about 1 °C. Errors are relatively larger for minimum compared to maximum temperatures, for the interior of the Alps compared to the flatland and for winter compared to summer. Visual comparisons suggest that valley-scale inversions and föhn are more realistically captured in the new compared to existing datasets. The usefulness of the presented dataset (SPARTACUS) is illustrated in preliminary analyses of long-term trends in climate impact indices. These reveal spatially variable and eventually considerable changes in the thermal climate in Austria.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Auer I, Böhm R, Mohnl H (1989) Klima von Wien. Eine anwendungsorientierte Klimatographie. Magistrat der Stadt Wien, Vienna

  2. Auer I, Böhm R, Jurkovic A, Lipa W, Orlik A, Potzmann R, Schöner W, Ungersböck M, Matulla C, Briffa K, Jones PD, Efthymiadis D, Brunetti M, Nanni T, Maugeri M, Mercalli L, Mestre O, Moisselin JM, Begert M, Müller-Westermeier G, Kveton V, Bochnicek O, Stastny P, Lapin M, Szalai S, Szentimrey T, Cegnar T, Dolinar M, Gajic-Capka M, Zaninovic K, Majstorovic Z, Nieplova E (2007) HISTALP—historical instrumental climatological surface time series of the Greater Alpine Region 1760–2003. Int J Climatol 27:17–46. doi:10.1002/joc.1377

    Article  Google Scholar 

  3. Bénichou P, Le Breton O (1987) Prise en compte de la topographie pour la cartographie des champs pluviométrique. La Météorologie 7:23–34

    Google Scholar 

  4. Bica B, Steinacker R, Lotteraner C, Suklitsch M (2007) A new concept for high resolution temperature analysis over complex terrain. Theor Appl Climatol 90:173–183. doi:10.1007/s00704-006-0280-2

    Article  Google Scholar 

  5. Bukovsky MS, Karoly DJ (2007) A brief evaluation of precipitation from the North American regional reanalysis. J Hydrometeorol 8:837–846. doi:10.1175/JHM595.1

    Article  Google Scholar 

  6. Caussinus H, Mestre O (2004) Detection and correction of artificial shifts in climate series. J R Stat Soc Ser C Appl Stat 53:405–425. doi:10.1111/j.1467-9876.2004.05155.x

    Article  Google Scholar 

  7. Chimani B, Matulla C, Böhm R, Hofstätter M (2013) A new high resolution absolute temperature grid for the Greater Alpine Region back to 1780. Int J Climatol 33:2129–2141. doi:10.1002/joc.3574

    Article  Google Scholar 

  8. Daly C, Neilson RP, Phillips DL (1994) A statistical-topographical model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33:140–158. doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2

    Article  Google Scholar 

  9. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064. doi:10.1002/joc.1688

    Article  Google Scholar 

  10. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Willett KM, Aguilar E, Brunet M, Caesar J, Hewitson B, Jack C, Klein Tank AMG, Kruger AC, Marengo J, Peterson TC, Renom M, Oria Rojas C, Rusticucci M, Salinger J, Elrayah AS, Sekele SS, Srivastava AK, Trewin B, Villarroel C, Vincent LA, Zhai P, Zhang X, Kitching S (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res Atmos 118:2098–2118. doi:10.1002/jgrd.50150

    Article  Google Scholar 

  11. Frei C (2014) Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances. Int J Climatol 34:1585–1605. doi:10.1002/joc.3786

    Article  Google Scholar 

  12. Frei C, Schär C (2001) Detection probability of trends in rare events: theory and application to heavy precipitation in the Alpine region. J Clim 14:1564–1584. doi:10.1175/1520-0442(2001)014<1568:DPOTIR>2.0.CO;2

    Article  Google Scholar 

  13. Haiden T, Kann A, Wittmann C, Pistotnik G, Bica B, Gruber C (2011) The integrated nowcasting through comprehensive analysis (INCA) system and its validation over the Eastern Alpine Region. Weather Forecast 26:166–183. doi:10.1175/2010WAF2222451.1

    Article  Google Scholar 

  14. Hasenauer H, Merganicova K, Petritsch R, Pietscha SA, Thornton PE (2003) Validating daily climate interpolations over complex terrain in Austria. Agric For Meteorol 119:87–107. doi:10.1016/S0168-1923(03)00114-X

    Article  Google Scholar 

  15. Haslinger K, Anders I, Hofstätter M (2013) Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the Greater Alpine Region. Clim Dyn 40:511–529. doi:10.1007/s00382-012-1452-7

    Article  Google Scholar 

  16. Haslinger K, Koffler D, Schöner W, Laaha G (2014) Exploring the link between meteorological drought and streamflow: effects of climate-catchment interaction. Water Resour Res 50:2468–2487. doi:10.1002/2013WR015051

    Article  Google Scholar 

  17. Hiebl J, Auer I, Böhm R, Schöner W, Maugeri M, Lentini G, Spinoni J, Brunetti M, Nanni T, Perčec-Tadić M, Bihari Z, Dolinar M, Müller-Westermeier G (2009) A high-resolution 1961–1990 monthly temperature climatology for the greater Alpine region. Meteorol Z 18:507–530. doi:10.1127/0941-2948/2009/0403

    Article  Google Scholar 

  18. Huss M, Farinotti D, Bauder A, Funk M (2008) Modelling runoff from highly glacierized alpine drainage basins in a changing climate. Hydrol Process 22:3888–3902. doi:10.1002/hyp.7055

    Article  Google Scholar 

  19. Isotta FA, Vogel R, Frei C (2014) Evaluation of European regional reanalyses and downscalings for precipitation in the Alpine region. Meteorol Z. doi:10.1127/metz72014/0584

  20. Kapeller S, Lexer MJ, Geburek T, Hiebl J, Schueler S (2012) Intraspecific variation in climate response of Norway spruce in the eastern Alpine range: selecting appropriate provenances for future climate. For Ecol Manag 271:46–57. doi:10.1016/j.foreco.2012.01.039

    Article  Google Scholar 

  21. Katz RW, Brown BG (1992) Extreme events in a changing climate: variability is more important than averages. Clim Chang 21:289–302. doi:10.1007/BF00139728

    Article  Google Scholar 

  22. Klein Tank AMG, Zwiers FW, Zhang X (2009) Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. World Meteorol Organ. http://eca.knmi.nl/documents/WCDMP_72_TD_1500_en_1.pdf. Accessed 18 Sept 2014

  23. Luhamaa A, Kimmel K, Männik A, Rõõm R (2011) High resolution re-analysis for the Baltic Sea region during 1965–2005 period. Clim Dyn 36:727–738. doi:10.1007/s00382-010-0842-y

    Article  Google Scholar 

  24. Massmann C, Holzmann H (2012) Analysis of the behavior of a rainfall-runoff model using three global sensitivity analysis methods evaluated at different temporal scales. J Hydrol 475:97–110. doi:10.1016/j.jhydrol.2012.09.026

    Article  Google Scholar 

  25. Masson D, Frei C (2014) Long-term variations and trends of mesoscale precipitation in the Alps: Recalculation and update for 1901–2008. Int J Climatol (submitted)

  26. Mestre O, Gruber C, Prieur C, Caussinus H, Jourdain S (2011) SPLIDHOM, a method for homogenization of daily temperature observations. J Appl Meteorol Climatol 50:2343–2358. doi:10.1175/2011JAMC2641.1

    Article  Google Scholar 

  27. Nemec J, Gruber C, Chimani B, Auer I (2013) Trends in extreme temperature indices in Austria based on a new homogenised dataset. Int J Climatol 33:1538–1550. doi:10.1002/joc.3532

    Article  Google Scholar 

  28. Olefs M, Schöner W, Suklitsch M, Wittmann C, Niedermoser B, Neururer A, Wurzer A (2013) SNOWGRID—a new operational snow cover model in Austria. International Snow Science Workshop Proceedings 2013:38–45. http://arc.lib.montana.edu/snow-science/item/1785. Accessed 18 Sept 2014

  29. Petritsch R, Hasenauer H (2014) Climate input parameters for real-time online risk assessment. Nat Hazards 70:1749–1762. doi:10.1007/s11069-011-9880-y

    Article  Google Scholar 

  30. R Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN: 3-900051-07-0. http://www.R-project.org. Accessed 18 Sept 2014

  31. Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK, Bloom S, Chen J, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648. doi:10.1175/JCLI-D-11-00015.1

    Article  Google Scholar 

  32. Ruefenacht B, Finco MV, Nelson MD, Czaplewski R, Helmer EH, Blackard JA, Holden GR, Lister AJ, Salajanu D, Weyermann D, Winterberger K (2008) Conterminous U.S. and Alaska forest type mapping using forest inventory and analysis data. Photogramm Eng Remote Sens 74:1379–1388

    Article  Google Scholar 

  33. Schöner W, Dos Santos Cardoso E (2004) Datenbereitstellung, Entwicklung von Regionalisierungstools und einer Schnittstelle zu den regionalen Klimamodellen. Zentralanstalt für Meteorologie und Geodynamik. http://foresight.ait.ac.at/SE/projects/reclip/reports/report6_Regionalisierung_ZAMG.pdf. Accessed 18 Sept 2014

  34. Seidl R, Schelhaas MJ, Lindner M, Lexer MJ (2009) Modelling bark beetle disturbances in a large scale forest scenario model to assess climate change impacts and evaluate adaptive management strategies. Reg Environ Chang 9:101–119. doi:10.1007/s10113-008-0068-2

    Article  Google Scholar 

  35. Steinacker R, Ratheiser M, Bica B, Chimani B, Dorninger M, Gepp W, Lotteraner C, Schneider S, Tschannett S (2006) A mesoscale data analysis and downscaling method over complex terrain. Mon Weather Rev 134:2758–2771. doi:10.1175/MWR3196.1

    Article  Google Scholar 

  36. Steinacker R, Mayer D, Steiner A (2011) Data quality control based on self-consistency. Mon Weather Rev 139:3974–3991. doi:10.1175/MWR-D-10-05024.1

    Article  Google Scholar 

  37. Szentimrey T, Bihari Z, Lakatos M, Szalai S (2011) Mathematical, methodological questions concerning the spatial interpolation of climate elements. Időjárás 115:1–11

    Google Scholar 

  38. Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544. doi:10.1002/joc.2168

    Article  Google Scholar 

  39. Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251

    Article  Google Scholar 

  40. Umweltbundesamt (2014) CORINE Land Cover. http://www.umweltbundesamt.at/umweltsituation/raumordnung/flchen-entw/grundlagen/erdbeobachtung/corine. Accessed 18 Sept 2014

  41. Wienert U, Kreienkamp F, Spekat A, Enke W (2013) A simple method to estimate the urban heat island intensity in data sets used for the simulation of the thermal behaviour of buildings. Meteorol Z 22:179–185. doi:10.1127/0941-2948/2013/0397

    Article  Google Scholar 

  42. Žuvela-Aloise M, Koch R, Neureiter A, Böhm R, Buchholz S (2014) Reconstructing urban climate of Vienna based on historical maps dating to the early instrumental period. Urban Clim 10:490–508. doi:10.1016/j.uclim.2014.04.002

    Article  Google Scholar 

Download references

Acknowledgments

Special thanks are given to Michael Hofstätter for thematic discussion, to Walter Pawlica, Irena Grubi-Tonkovic, Marc Olefs and Ivonne Anders (all ZAMG) for technical support, to Christopher Thurnher and Hubert Hasenauer (Institute of Silviculture, BOKU) for customised provision of gridded data and to the ZAMG’s sister institutes in the neighbouring countries for unbureaucratic provision of station data: Luca Maraldo and Günther Geier (Hydrographisches Amt der autonomen Provinz Bozen – Südtirol), Daniel Wolf (MeteoSchweiz), Hermann Mächel (DWD), Petr Skalak (CHMI), Pavol Nejedlík (SHMU), Zita Bihari (OMSZ), Gregor Vertačnik and Mojca Dolinar (ARSO) and Andrea Cicogna (ARPA-OSMER FVG). Furthermore, we thank two anonymous reviewers for their constructive suggestions. The project SPARTACUS was funded by the Austrian Federal Ministry of Science and Research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Johann Hiebl.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hiebl, J., Frei, C. Daily temperature grids for Austria since 1961—concept, creation and applicability. Theor Appl Climatol 124, 161–178 (2016). https://doi.org/10.1007/s00704-015-1411-4

Download citation

Keywords

  • Interpolation Method
  • Interpolation Error
  • Background Field
  • Mean Absolute Error
  • Gridded Dataset