Climate variability and change over Uzbekistan – an analysis based on high resolution CHELSA data

Makhmud Khaydarov1, Lars Gerlitz2*

1Centre of Hydrometeorological Service of The Republic of Uzbekistan – Uzhydromet

2Helmholtz Centre Potsdam, German Research Centre for Geosciences, Section 4.4 Hydrology

*Corresponding author:

Scientific Article


Climate change and variability represent a major risk for the Central Asian economies, particularly for the agriculture and energy sectors. A detailed analysis of recent climatic changes is required in order to assess their impact. However, the observational network in Central Asia has significantly degraded after the dissolution of the Soviet Union, especially in the vulnerable high mountain regions (Unger-Shayesteh et al. 2013). Currently, the hydro-meteorological network of Uzhydromet is being modernized. Gridded climate data with high resolution represent a great opportunity to overcome these data related challenges, since they are freely available to all scientists andpractitioners and enable a cross-border monitoring of climatic conditions. Here the authors introduce the CHELSA (“Climatologies at high resolution for the earth’s land surface areas“) data set, which provides monthly data of precipitation and temperature or the entire globe at a resolution of 1*1 km for the period 1979-2013. The data set is evaluated against 20 stations across Uzbekistan. The authors show that CHELSA agrees in most cases with station observations, which is manifested in small deviations of mean seasonal temperature and precipitation sums. Further, temperature and precipitation trends as well as the relationship with large-scale atmospheric circulation modes, such as the El Niño Southern Oscillation and the North Atlantic Oscillation, are well represented by the CHELSA data set. The authors conclude, that CHELSA is a reasonable basis for the investigation of climate variability and change in Central Asia.

Download the article

For citation: Khaydarov, M., & Gerlitz, L. (2019). Climate variability and change over Uzbekistan – an analysis based on high resolution CHELSA data. Central Asian Journal of Water Research, 5(2), 1–19.


  1. Aleksandrova, M., Gain, A. K., and Giupponi, C., 2016, Assessing agricultural systems vulnerability to climate change to inform adaptation planning: an application in Khorezm, Uzbekistan. Mitigation and Adaptation Strategies for Global Change, Vol. 21, pp. 1263–1287. Available at:
  2. Barlow, M., Zaitchik, B., Paz, S., Black, E., Evans, J., and Hoell, A., 2015, A Review of Drought in the Middle East and Southwest Asia. Journal of Climate. Available at:
  3. Chen, F., Huang, W., Jin, L., Chen, J., and Wang, J., 2011, Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming. Science China Earth Sciences, Vol. 54, pp. 1812–1821. Available at:
  4. Chub, V. E., 2007, Climate change and its impact on hydrometeorological processes, agroclimatic and water resources of the Republic of Uzbekistan. Internal report at UzHydromet.
  5. Chub, V., and Spectorman, Y., 2016, Climate Trends in Uzbekistan: Climate Change, Reasons, Impacts and Response Measures. UzHydromet Bulletin No. 10, Tashkent.
  6. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L. Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. -J., Park, B. -K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. -N., Vitart, F., 2011, The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quaterly Journal of the Meteorological Society, Vol. 137, pp. 553–597. Available at:
  7. Gerlitz, L., Vorogushyn, S., Apel, H., Gafurov, A., Unger-Shayesteh, K., and Merz, B. 2016, A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia. Hydrology and Earth System Sciences, Vol. 20, pp. 4605–4623. Available at:
  8. Gerlitz, L., Steirou, E., Schneider, C., Moron, V., Vorogushyn, S., and Merz, B., 2018, Variability of the Cold Season Climate in Central Asia. Part I: Weather Types and Their Tropical and Extratropical Drivers. Journal of  Climate, Vol. 31, pp. 7185–7207. Available at:
  9. Giese, E., Mossig, I., Rybski, D., and Bunde, A., 2007, Long-Term Analysis of Air Temperature Trends in Central Asia (Analyse langjähriger Zeitreihen der Lufttemperatur in Zentralasien). Erdkunde, Vol. 61, pp. 186–202. (in German).
  10. Hess, A., Iyer, H., and Malm, W., 2001, Linear trend analysis: a comparison of methods. Atmospheric Environment, Vol. 35, pp. 5211–5222. Available at:
  11. Hurrell, J. W., 1995, Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation. Science, Vol. 269, pp. 676–679. Available at:
  12. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi:10.1017/CBO9781107415324.
  13. Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Wilber, R., Zimmermann, N, Linder, H., Kessler, M., 2017, Climatologies at high resolution for the earth’s land surface areas. Scientific Data, Vol. 4, Article Nr: 170122. Available at:
  14. Kendall, M. G., 1938, A New Measure of Rank Correlation. Biometrika, Vol. 30, pp. 81–93, Available at:
  15. R Development Core Team, 2008, R: The R Project for Statistical Computing, R Foundation for Statistical Computing. Available at: (Accessed December 17, 2015).
  16. Rasmusson, E. M., and Wallace, J. M., 1983, Meteorological Aspects of the El Niño/Southern Oscillation. Science, Vol. 222, pp. 1195–1202. Available at:
  17. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and Rudolf, B., 2014, GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology, Vol. 115, pp. 15–40. Available at:
  18. Siegfried, T., Bernauer, T., Guiennet, R., Sellars, S., Robertson, A. W., Mankin, J., Bauer-Gottwein, P., and Yakovlev, A., 2012, Will climate change exacerbate water stress in Central Asia? Climatic Change, Vol. 112, pp. 881–899. Available at:
  19. Syed, F. S., Giorgi, F. Pal, J. S., and King, M. P., 2006, Effect of remote forcings on the winter precipitation of central southwest Asia part 1: observations. Theoretical and Applied Climatology, Vol. 86, pp. 147–160. Available at:
  20. Syed, F. S., Giorgi, F., Pal, J. S., King, M.P., and Keay, K., 2010, Regional climate model simulation of winter climate over Central–Southwest Asia, with emphasis on NAO and ENSO effects. International Journal of Climatology, Vol. 30, pp. 220–235. Available at:
  21. Unger-Shayesteh, K., Vorogushyn, S., Farinotti, D., Gafurov, A., Duethmann, D., Mandychev, A., and Merz, B., 2013, What do we know about past changes in the water cycle of Central Asian headwaters? A review. Global and Planetary Change, Vol. 110, Part A, pp. 4–25. Available at:
  22. Uzhydromet and UNEP, 2016, Third National Communication of the Republic of Uzbekistan under the United Nations Framework Convention on Climate Change. UN Framework Convention on Climate Change (UNFCCC).

climate change, gridded data, political economy, trend analysis, Uzbekistan, water insecurity

Publication Alerts: