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

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

References

  1. Beersma JJ, Buishand TA (2003) Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Clim Res 25:121–133

    Article  Google Scholar 

  2. Dubrovsky M, Zalud Z, Stastna M (2000) Sensitivity of ceres-maize yields to statistical structure of daily weather series. Clim Change 46:447–472

    Article  Google Scholar 

  3. Dubrovsky M, Buchtele J, Zalud Z (2004) High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Clim Change 63:145–179

    Article  Google Scholar 

  4. Dubrovsky M, Metelka L, Semeradova D, Trnka D, Halasova O, Ruzicka M, Nemesova I, Kliegrova S, Zalud Z (2006) The CaliM&Ro project: calibration of Met&Roll Weather generator for sites without or with incomplete meteorological observations. In: Weather Typets Classifications, Proc. 5th EMS Annual Meeting, Session AW8, O.E. Tveito and M. Pasqui, Eds. p.98-107

  5. Dubrovský M, Hayes M, Duce P, Trnka M, Svoboda M, Zara P (2014) Multi-GCM projections of future drought and climate variability indicators for the Mediterranean region. Reg Environ Change 14:1907–1919

    Article  Google Scholar 

  6. Frost AJ, Charles SP, Timbal B, Chiew FHS, Mehrotra R, Nguyen KC, Chandler RE, McGregor JL, Fu G, Kirono DGC, Fernandez E, Kent DM (2011) A comparison of multi-site daily rainfall downscaling techniques under Australian conditions. J Hydrol 408(1):1–18

    Article  Google Scholar 

  7. Gutierrez J, Maraun D, Widmann M, Huth R, Hertig E, Benestad R, Roessler O, Wibig J, Wilcke R, Kotlarski S, San-Martın D, Herrera S, Bedia J, Casanueva A, Manzanas R, Iturbide M, Vrac M, Dubrovsky M, Ribalaygua J, Portoles J, Raty O, Raisanen J, Hingray B, Raynaud D, Casado M, Ramos P, Zerenner T, Turco M, Bosshard T, Stepanek P, Bartholy J, Pongracz R, Keller D, Fischer A, Cardoso R, Soares P, Czernecki B, Page C (2018) An intercomparison of a large ensemble of statistical downscaling methods over Europe: results from the VALUE perfect predictor cross-validation experiment. Int J Climatol 2018:1–36. https://doi.org/10.1002/joc.5462

    Article  Google Scholar 

  8. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res (Atmospheres) 113:D20119. https://doi.org/10.1029/2008JD10201

    Article  Google Scholar 

  9. Hu Y, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the yellow river source region in china: a comparison of three statistical downscaling methods. Theor Appl Climatol 112(3-4):447–460

    Article  Google Scholar 

  10. Huth R, Dubrovsky M (2019) Testing for trends on a regional scale: beyond local significance. Submitted for publication in J Clim

  11. Jacob D, Petersen J, Eggert B et al (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14:563–578

    Article  Google Scholar 

  12. Keller DE, Fischer AM, Frei C, Liniger MA, Appenzeller C, Knutti R (2015) Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment. Hydrol Earth Syst Sci 19:2163–2177. https://doi.org/10.5194/hess-19-2163-2015

    Article  Google Scholar 

  13. Keller DE, Fischer AM, Liniger MA, Appenzeller C, Knutti R (2017) Testing a weather generator for downscaling climate change projections over Switzerland. Int J Climatol 37:928–942. https://doi.org/10.1002/joc.4750

    Article  Google Scholar 

  14. Kerkhoff C, Künsch HR, Schär C (2014) Assessment of bias assumptions for climate models. J Clim 27:6799–6818

    Article  Google Scholar 

  15. Kysely J, Kim J (2009) Mortality during heat waves in South Korea, 1991 to 2005: how exceptional was the 1994 heat wave ? Clim Res 38:105–116. https://doi.org/10.3354/cr00775

    Article  Google Scholar 

  16. Kysely J, Plavcova E (2012) Declining impacts of hot spells on mortality in the Czech Republic, 1986–2009: adaptation to climate change? Clim Change 113:437–453. https://doi.org/10.1007/s10584-011-0358-4

    Article  Google Scholar 

  17. Maraun D (2016) Bias correcting climate change simulations—a critical review. Curr Clim Change Rep 2: 211–220. [DOI https://doi.org/10.1007/s40641-016-0050-x]

    Article  Google Scholar 

  18. Maraun D, Huth R, Gutiérrez JM, San Martín D, Dubrovsky M, Fischer A, Hertig E, Soares PMM, Bartholy J, Pongrácz R, Widmann M, Casado MJ, Ramos P (August 2017) Bedia J (2017) The VALUE perfect predictor experiment: evaluation of temporal variability. Int J Climatol. https://doi.org/10.1002/joc.5222

    Article  Google Scholar 

  19. Rajagopalan B, Lall U (1999) A k-nearest-neighbor simulator for daily precipitation and other variables. Water Resources Res 35:3089–3101

    Article  Google Scholar 

  20. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Res 17:182–190

    Article  Google Scholar 

  21. Rötter RP, Palosuo T, Pirttioja NK, Dubrovsky M, Salo T, Fronzek S, Aikasalo R, Trnka M, Ristolainen A, Carter TR (2011) What would happen to barley production in Finland if global warming exceeded 4 °C? A model-based assessment. Europ J Agronomy 35:205–214

    Article  Google Scholar 

  22. Santos JA, Karremann MK, Jones GV, Pinto JG (2013) Ensemble projections for wine production in the Douro Valley of Portugal. Clim Change 117:211–225

    Article  Google Scholar 

  23. Semenov MA (2008) Simulation of extreme weather events by a stochastic weather generator. Clim Res 35:203–212

    Article  Google Scholar 

  24. Solow AR (1988) Detecting changes through time in the variance of a long-term hemispheric temperature record: an application of robust locally weighted regression. J Clim 1:290–296

    Article  Google Scholar 

  25. Supit I, Van Diepen CA, de Wit AJW, Wolf J, Kabat P, Baruth B, Ludwig F (2012) Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator. Agric Forest Meteorol 164:96–111

    Article  Google Scholar 

  26. Trnka M, Eitzinger J, Dubrovsky M, Semeradova D, Stepanek P, Hlavinka P, Balek J, Skalak P, Farda A, Formayer H, Zalud Z (2010) Is rainfed crop production in central Europe at risk? Using a regional climate model to produce high resolution agroclimatic information for decision makers. J Agricultural Sci 148:639–656

    Article  Google Scholar 

  27. van der Schrier G, Briffa KR, Jones PD, Osborn TJ (2006) Summer moisture variability across Europe. J Clim 19:2818–2834

    Article  Google Scholar 

  28. Verdin A, Rajagopalan B, Kleiber W, Katz RW (2014) Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environ Res Risk Assess 29:347–356

    Article  Google Scholar 

  29. Widmann M, Bedia J, Gutierrez JM, Bosshard T, Hertig E, Maraun D, Casado MJ, Ramos P, Cardoso RM, Soares PMM, Ribalaygua J, Page C, Fischer A, Herrera S, and Huth R (2019) Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment. Accepted for publication in Int J Climatol

  30. Wilks DS (1992) Adapting stochastic weather generation algorithms for climate change studies. Clim Change 22:67–84

    Article  Google Scholar 

  31. Wilks DS (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydrol 210:178–191. https://doi.org/10.1016/S0022-1694(98)00186-3

    Article  Google Scholar 

  32. Wilks DS (2008) High-resolution spatial interpolation of weather generator parameters using local weighted regressions. Agric Forest Meteorol 148:111–120

    Article  Google Scholar 

  33. Wilks DS (2009) A gridded multisite weather generator and synchronization to observed weather data. Water Resources Res 45:1–11

    Article  Google Scholar 

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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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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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