Statistical Evaluation of Predicted Maximum and Minimum Temperatures with CLIGEN Climate Model

Year 2020, , 190 - 201, 31.12.2020

Bilal Habesi Özkaynar Saniye Demir Yunus Akdoğan

https://doi.org/10.13002/jafag4715

Abstract

Climate simulation models are widely used in generating estimated daily data to be used in climate change, soil erosion, water holding capacity, water quality, product development, and many other studies. Climate models are used to simulate the impact of future climate smülations in cases when long-term measured data is not sufficient, the measured data contain erroneous records since the collection of observed data is costly or requires a lot of time. Most climate models predict one or more climate variables such as wind speed, relative humidity, solar radiation, temperature, and precipitation. Climate models such as the CLIGEN, USCLIMATE and the WXGEN create max and min temperature values using the standard normal distribution. In the present study, the CLIGEN climate model was used to simulate the long-term average temperature data for Kayseri, Sivas, and Yozgat meteorologic stations. The compliance of both observed and simulated data with the normal distribution was determined by the Kolmogorov-Smirnov test. It was observed that the maximum and minimum temperature values did not conform to the normal distribution, and the skew value was negative for almost all months. It was found that the CLIGEN simulated above the observed value for the summer months and the values obtained for some months showed the normal distribution.

Keywords

CLIGEN, Climate, Kolmogorov-Smirnov, Simulation, Temperature

References

• Arnell, N. W., Reynard, N. S.,1996. The Effects of Climate Change Due To Global Warming on River Flows in Great Britain. Journal of Hydrology, 183(3–4), 397–424.
• Bindraban, P. S., Coauthors., 2012. Assessing the İmpact of Soil Degradation on Food Production. Current Opinion in Environmental Sustainability, 4, 478–488.
• Chapin III, F. S., J. McFarland, A. D. McGuire, E. S. Euskirchen, R. W. Ruess, K. Kielland. 2009. The Changing Global Carbon Cycle: Linking Plant–Soil Carbon Dynamics to Global Consequences. Journal of Ecology, 97:840-850.
• Corder, G.W., Foreman, D.I., 2009. Nonparametric Statistics: an İntroduction. Nonparametric Stat. Non-Statisticians a Step-by-Step Approach. Hoboken Wiley.
• Demir, S., 2016. Estimation of Surface Flow and Soil Loss Using Wepp Hillslope Model in Tokat Province. Gaziosmanpaşa University. PhD thesis in Institute of Science.
• Demir, S., Yürekli, K., Oğuz, İ., Erdoğan, M., 2020. Farklı İklim Modelleri Kullanılarak Tokat İli’nde Kuraklık Analizi. Gaziosmanpasa Journal of Scientific Research. Volume 9, Nuımber 1, Pages:37-46.
• Feidas H, Makrogiannis T, Bora-Senta E. 2004. Trend Analysis of Air Temperature Time Series in Greece and Their Relationship with Circulation using Surface and Satellite Data: 1955-2001. Theoretical and Applied Climatology 79: 185–208.,
• Frich, P., Alexander, L. V., Della-Marta, P. M., Gleason, B., Haylock, M., Tank, A. K., Peterson, T., 2002. Observed Coherent Changes in Climatic Extremes During The Second Half of The Twentieth Century. Climate Research, 19(3), 193–212.
• Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea,D.W., Medina-Elizade,M. 2006. Global Temperature Change. PNAS. 103 (39):14288-14293.
• Hanson, C. L., K. A. Cumming, D. A. Woolhiser, and C. W. Richardson. 1994. Microcomputer Program for Daily Weather Simulation in the Contiguous United States. USDA-ARS-114. Washington, D.C.: USDA.
• Kaushal N, Bhandari K, Siddique, KHM., Nayyar, H. 2016. Food Crops Face Rising Temperatures: an Overview of Responses, Adaptive Mechanisms, and Approaches to İmprove Heat Tolerance. Cogent Food Agric 2(1):1–42.
• Kleın Tank, A.M.G., Konnen, G.P. 2003. Trends in Indices of Daily Temperature and Precipitation Extremes in Europe, 1946–99. American Meteorological Society. Volume 16.
• Kumar, K: K., Patwardhan, S.K. Kulkarni, A., Kamala, K., Rao, K.K., Jones, R. 2011. Simulated Projections for Summer Monsoon Climate over India by A High-Resolution Regional Climate Model (PRECIS). Curr. Sci.,101 (3): pp.312-326
• Kumar, V., Jain, S.K., Singh, Y., 2010. Analysis of long-term rainfall trends in India. Hydrol. Sci. J. 55 (4), 484–549.
• Lenderink, G, Van Meijgaard, E. 2010. Linking İncreases in Hourly Precipitation Extremes to Atmospheric Temperature and Moisture Changes. Environmental Research Letters.
• Maheras P, Patrikas I, Karacostas T, Anagnostopoulou C. 2000. Automatic Classification of Circulation Types in Greece: Methodology, Description, Frequency, Variability and Trend Analysis. Theoretical and Applied Climatology 67: 205–223.
• Mayhew, P. J., . Jenkins, G. B., Benton,T. B., 2008. A Long-Term Association Between Global Temperature and Biodiversity, Origination and Extinction in The Fossil Record. Procedings of the Royal Society B, 275, 47–53.
• Martinkova, M., Hanel, M., 2016. Evaluation of Relations Between Extreme Precipitation and Temperature in Observational Time Series from the Czech Republic. Hindawi Publishing Corporation Advances in Meteorology. Article ID 2975380,
• Nicks, A. D., Harp, J. F., 1980. Stochastic Generation of Temperature and Solar Radiation Data. Journal of Hydrology, 48(1-2, 1-7.
• Nicks, A. D., Gander G. A., 1994. A Weather Generator for Climate Inputs to Water Resource and Other Models. In: Computers in Agriculture. Michihan 49085, USA.
• Nicks AD, Lane LJ, Gander GA., 1995. Weather generator, Ch. 2. In: Flanagan D.C, and Nearing MA. USDA−Water Erosion Prediction Project: Hillslope Profile and Watershed Model Documentation, NSERL Report No. 10. West Lafayette, Ind.: USDA−ARS−NSERL.
• Rahman, M. A., Yunsheng, L., Sultana, N., 2017. Analysis and Prediction of Precipitation Trends over Bangladesh Using Mann–Kendall, Spearman’s Rho Tests and ARIMA Model. Meteorology and Atmospheric Physics, 129(4), 409–424.
• Raggad, B., 2018. Statistical assessment of changes in extreme maximum temperatures over Saudi Arabia, 1985–2014. Theor Appl Climatol. 132:1217–1235
• Richardson, C.W., 1981. Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation. Water Resour Research; 17: 182-90.
• Richardson, C.W., Wright, D.A.,1984. WGEN: A Model for Generating Daily Weather Variables. U.S. Depart. Agr, Agricultural Research Service. Publ. ARS-8; p. 1-86.
• Romilly, P., 2005. Time Series Modeling of Global Mean Temperature for Managerial Decision-Making. Journal of Environmental Management, 76, 61–70.
• Salarpour, M., Rahman, N.A., Yusop, Z., 2011. Simulation of Flood Extent Mapping by İnfoWorks RS-Case Study for Tropical Catchment. J. Software Eng. 5:127-135.
• Semenov, M.A., Barrow, E.M., Lars, E.W., 2002. A Stochastic Weather Generator for Use in Climate Impact Studies, User Manual.
• Sharma, C., Ojha, C.S.P., 2019. Changes of Annual Precipitation and Probability Distributions for Different Climate Types of the World. Water. 2092.
• Stöckle CO, Nelson R, Donatelli M, Castellvì F., 2001. ClimGen: a Flexible Weather Generation Program. 2nd Int. Symp. Model. Crop. Syst., Florence, pp. 16–18
• Udo-Inyang, U. C.,Edem, I. D., 2012. A Changes in Precipitation Analysis of Precipitation Trends in Akwa Ibom State, Nigeria. Journal of Environmental and Earth Science, 2(8), 60–71.
• Ye, L., Tang, H., Zhu, Verdoodt, J. A., Van Ranst, E., 2008. Spatial Patterns and Effects of Soil Organic Carbon on Grain Productivity Assessment in China. Soil Use and Management, 24, 80–91.