Parametric gridded weather generator for use in present and future climates: focus on spatial temperature characteristics

Theoretical and Applied Climatology volume 139, pages1031–1044(2020)Cite this article

Abstract

This study presents results of the pilot experiments made with new parametric multi-site multi-variable stochastic daily weather generator (WG) SPAGETTA. The experiments are performed for eight European regions and we focus on spatial characteristics of temperature. The WG is calibrated using the gridded weather data E-OBS. In evaluating the generator, the spatial and temporal temperature autocorrelations derived from the synthetic series were found to perfectly fit the values derived from the calibration data. Next, the WG is validated in terms of the frequency of “spatial hot days” and the annual maximum length of “spatial hot spells”. The results indicate a very good correspondence between characteristics derived from synthetic and calibration data. As part of the validation tests, the performance of the WG is compared with a regional climate model (RCM), which shows a similar performance as the generator. In a final experiment, the use of the WG for the future climate is demonstrated, the WG parameters (including the temperature autocorrelations) calibrated with the observed data are modified according to the RCM-based changes in these parameters. While analyzing synthetic series produced with the modified generator, we discuss partial impacts due to changes in individual WG parameters on the spatial hot days and spells. We show that the impacts are mainly (but not only) due to changes in temperature averages. The projected changes in temperature autocorrelations have also some impacts, larger for the spatial hot spells than for the spatial hot days. Climate change impacts on spatial hot days/spells based on the WG are compared with impacts based on the RCM, and we conclude that the differences are mainly due to simplifying assumptions adopted in our pilot experiment.

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Notes

  1. 1.

    In the “SPAGETTA” acronym, SPAGET stands for “SPAtial GEneraTor”, and “TA” has two interpretations related to the two main motivations for developing the generator: Trend Analysis and Tyrolian Alps (Oetztal, which is the target region of the HydroGem3 project (co-led by one of the authors, M.W.R.), for which the generator is also developed, lies in this region).

Abbreviations

WG:

Weather generator

CC:

Climate change

RCM:

Regional climate model

GCM:

Global climate model

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Acknowledgments

The present experiment was supported by the Czech Science Foundation, projects 16-04676S and 18-15958S, and the HydroGeM3 project financed by the Austrian Academy of Science (ÖAW). Meetings of the authors team were supported by the program “Scientific and Technological Cooperation between Austria and the Czech Republic—project CZ12/2016” (2017) of the Austrian Center for International Cooperation and Mobility (OeAD), and project 7AMB16AT020 of the Czech Ministry of Education, Youth and Sports (MSMT) (for the Czech side). We appreciate the free access to E-OBS data (http://www.ecad.eu/) and RCM data (we used https://climate4impact.eu to download the data).

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Affiliations

  1. The Czech Academy of Sciences, Institute of Atmospheric Physics, Bocni II, 141 00, Prague, Czech Republic

    Martin Dubrovsky & Radan Huth

  2. Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Prague, Albertov 6, 128 43, Prague, Czech Republic

    Radan Huth

  3. The Czech Academy of Sciences, Global Change Research Institute, Bělidla 986/4a, 603 00, Brno, Czech Republic

    Martin Dubrovsky

  4. Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innrain 52f, A-6020, Innsbruck, Austria

    Hetal Dabhi & Mathias W. Rotach

Authors
  1. Martin Dubrovsky
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  2. Radan Huth
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  3. Hetal Dabhi
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  4. Mathias W. Rotach
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Correspondence to Martin Dubrovsky.

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Dubrovsky, M., Huth, R., Dabhi, H. et al. Parametric gridded weather generator for use in present and future climates: focus on spatial temperature characteristics. Theor Appl Climatol 139, 1031–1044 (2020). https://doi.org/10.1007/s00704-019-03027-z

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