Applicability of GCMs for evaluation of agro-climatic properties of local territories

N.A. Lemeshko1, V.P. Evstigneev2,3,  A.P. Morozov1, V.A. Rusakov1

 1Saint-Petersburg State University, RF, Saint-Petersburg, Universitetskaya Emb., 7

2Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33

3Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28

E-mail: natlem@mail.ru

DOI: 10.33075/2220-5861-2021-3-23-30

UDC 551.581/.582.2

Abstract:

   The analysis of reliability and accuracy of simulation of the current climate by 18 GCM models included in CMIP6 is performed. The calculations based on the models are compared with the observation data for 175 meteorological stations of the Roshydromet network on the ETR for 1984-2014. The comparison is made for precipitation, air temperature and relative humidity of the air. The procedure for comparing model outputs with observational data includes: interpolation of the model results to the location of the nearest meteorological station; averaging of meteorological data for Köppen climatic zones and assessment of the accuracy of precipitation, temperature and humidity simulation. A system for ranking statistical estimates of the deviation “model – observation” according to six criteria is developed and the best models that most accurately reproduce the empirical data is selected. An ensemble is compiled from the best models and calculations of agro-climatic characteristics for the ETR are performed. The comparison of agro-climatic indicators calculated on the basis of the ensemble of models and observational data is carried out on the example of the territory of the Upper Volga region, including the Yaroslavl, Kostroma, Vologda, Novgorod and Tver regions. Calculations have shown that the ensemble of GCMs quite reliably reproduce the spatial features of the distribution of agro-climatic indicators based on air temperature (the sum of active air temperatures and the duration of the growing season). Less reliable are the results of the models for complex parameters that take into account the humidification mode (G. T. Selyaninov’s hydrothermal coefficient).

Keywords: ensemble of models, GCMs, model rank, air temperature, precipitation, agro-climatic indicators.

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REFERENCES

  1. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 2014. 151 p.
  2. Report on the peculiarities of the climate on the territory of the Russian Federation for 2019. Moscow, 2020. 97 p.
  3. Meleshko V.P., and Govorkova V.A. The success of calculating the modern regional climate using an ensemble of CMIP3 and CMIP5 models. Trudy GGO, 2013, Issue 568, pp. 26–50.
  4. Pavlova V.N. Agro-climatic resources and agricultural productivity of Russia in the implementation of new climate scenarios in the XXI century. Trudy GGO, 2013, Issue 569, pp. 20–37.
  5. https://esgf-node.llnl.gov/projects/cmip6/ (data obrashcheniya: 25.08.2021).
  6. Volodin E.M., Mortikov E.V., and Kostrykin S.V. Reproduction of the modern climate in a new version of the climate model of the IVM RAS. Izv. RAS. Physics of the atmosphere and the ocean, 2017, Vol. 53, No. 2, pp. 164–178.
  7. https://meteoinfo.ru/images/media/books-docs/WMO/ensembles-wmo_1091_ru.pdf. (accessed: 05.09.2021).
  8. http://aisori-m.meteo.ru/waisori/select.xhtml / (accessed: 20.08.2021).
  9. Menzhulin G.V., and Galakhova Yu.E. Estimates of the reliability of model scenarios of global climate changes in application to the problems of calculating their impact on environmental processes. All-Russian quarterly magazine. Protection of atmospheric air. Atmosphere, 2012, Issue 2, pp. 48–56.
  10. Duchon J. Splines minimizing rotation invariant semi-norms in Sobolev spaces. Proceedings of a Conference Held at Oberwolfach April 25 – May 1, 1976. Berlin: Springer, 1976, pp. 85–100.
  11. Hengl T., Heuvelink G., and Rossiter D. About regression-kriging: from equations to case studies. Computers & Geosciences, 2007, Vol. 33, pp. 1301–1315.
  12. https://docs.rstudio.com (accessed: 25.08.2021).
  13. Evstigneev V.P., and Morozov A.P. Assessment of the prospects for using the MOCAI data for studying the climate of the European territory of Russia. In the collection: Environmental activity and environmental education: a regional aspect. Materials of the All-Russian Scientific Conference. St. Petersburg, 2020, pp. 50–54.
  14. Lemeshko N.A., and Belokopytova M.A. Analysis of the reliability and accuracy of modern model climate scenarios for the south of the European territory of Russia. In the collection: Environmental activity and environmental education: a regional aspect. materials of the All-Russian scientific Conference. St. Petersburg, 2020, pp. 136–140.
  15. Selyaninov G.T. On agricultural climate assessment. Proceedings on agricultural meteorology, 1928, Issue 20, pp. 169–178.
  16. Sirotenko O.D., Abashina E.V., and Pavlova V.N. Dynamics of climate-conditioned changes in heat supply, moisture content and productivity of the agricultural zone of Russia. Proceedings of the Federal State Budgetary Institution “VNNISHM”, 2013, Issue 38, pp. 41–53.
  17. Gringof I.G., and Kleshchenko A.D. Fundamentals of agricultural meteorology. Vol. I. Obninsk: FSBI “VNIIGMI-MCD”, 2011, 808 p.

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