On Some Climatic Scenarios For Turkey From The Perspective of Changes in the Annual Mean Temperatures via Aggregation by Steady-State Distribution

Year 2018, Volume: 5 Issue: 2, 197 - 217, 01.08.2018

Salih Çelebioğlu

https://doi.org/10.30897/ijegeo.426733

Cited By: 1

Abstract

In this study, it is
tried to give a system of Markov chains approach to the trend of annual mean
temperatures in Turkey.
For this reason, a data of annual mean temperatures between the years 1965 -
2012 of 58 meteorological stations of Turkey are used. Each scenario is
given as a solution to a quadratic programming problem for which is spanned by
the transition matrices of twelve groups. The steady-state distribution method
given here facilitates the multiple station Markov chain applications. In the
meantime, the linear regression approaches in which the averages of station
groups are considered as independent variables are as well introduced. It is
also given comments on some scenarios while an extreme scenario as a solution
to a few problems is pointed out to which is feared in respect of climatic change. 

Keywords

Annual mean temperatures, climatic change, Markov chain, Aggregation by steady-state distribution, Linear regression

References

• 1. Anisimov, V.V. (2008), Switching Processes in Queueing Models, John Wiley, Hoboken, NJ.
• 2. Ball, J.E., Luk, K.C.(1998), Modeling spatial variability of rainfall over a catchment, J. Hydrol. Eng., 3, 122-130. https://doi.org/10.1061/(ASCE)1084-0699(1998)3:2(122)
• 3. Boucherie, R.J. and van Dijk, N.M. (Eds.), Markov Decision Processes in Practice, International Series in Operations Research & Management Science, Springer, 2017.
• 4. Can, A., Atimtay, A.T.(2002), Time series analysis of mean temperature data in Turkey, Applied Time Series, 4, 20-23.
• 5. Cao, X.R., Ren, Z.Y., Bhatnagar, S., Fu, M., and Marcus, S. (2002), A time aggregation approach to Markov decision processes, Automata, 38: 929-943.
• 6. Clark, E.B.(2008), A framework for modelling stochastic optimisation algorithms with Markov chains, PhD Thesis, University of York, Department of Electronics, November, 2008.
• 7. Dessler, A.E., Introduction to Modern Climate Change, Cambridge University Press, 2012.
• 8. Dobrovolski, S.G. (2000), Stochastic Climate Theory: Models and Applications, Berlin, Springer.
• 9. Dogan, M., Ulke, A., Cigizoglu, H.K.(2015), Trend direction changes of Turkish temperature series in the first half of 1990s, Theor. Appl. Climatol., 121:23-29.
• 10. Floudas, C.A., Visweswaran, V. (1995), Quadratic optimization, In: Horst R., Pardalos P.M., Handbook of Global Optimization, Vol 2. Springer, Kluwer, Dordrecht, pp 217-269.
• 11. Imkeller, P. and von Storch, J.-S. (Eds.), Stochastic Climate Models. Progress in Probability, Springer, Basel, Birkhäuser Verlag, 2001.
• 12. Kadioglu, M.(1997), Trends in surface air temperature data over Turkey, Int. J. Climatol., 17, 511-520.
• 13. Karaburun, A., Demirci, A., Kara, F.(2012), Analysis of spatially distributed annual, seasonal and monthly temperatures in Marmara region from 1975 to 2006, Ozean Journal of Applied Sciences, 5(2), 131-149.
• 14. Stein, M.L.(1999), Interpolation of Spatial Data: Some Theory for Kriging, Springer.
• 15. Tayanc, M., Dalfes, H.N., Karaca, M., Yenigun, O.(1998), A comparative assessment of different methods for detecting inhomogeneities in Turkish temperature data set, Int. J. Climatol., 18, 561-578.
• 16. Turkish General Directorate of Meteorology Weather Station (2012) Databanks of Turkish General Directorate of Meteorology, Ankara
• 17. Ustaoglu, B.(2012), Trend analysis of annual mean temperature data using Mann-Kendall rank correlation test in Catalca-Kocaeli peninsula, northwest of Turkey for the period of 1970-2011, IBAC, 2, 276-287. http://dspace.epoka.edu.al/handle/1/379
• 18. von Storch, H. and Navarra, A. (Eds.), Analysis of Climate Variability: Applications of Statistical Techniques, Berlin, Springer Verlag, 1995.
• 19. Yin, G.G. and Zhang, Q. (2013), Continuous- Time Markov Chains and Applications: A- Two- Time Scale Approach, Second Edition, Springer.