Journals / Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi / 2020 / Cilt: 37 - Sayı: 3

Statistical Evaluation of Predicted Maximum and Minimum Temperatures withCLIGEN Climate Model

CLIGEN İklim Modeli ile Tahmin Edilen Maksimum ve Minimum Sıcaklıkların İstatistiksel Değerlendirilmesi

Pages
190–201
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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.

Özet

İklim simülasyon modelleri iklimsel değişiklikler, toprak erozyonu, su tutma kapasitesi, su kalitesi,ürün gelişimi ve benzeri birçok çalışmada kullanılmak üzere tahmini günlük verilerinin oluşturulmasındayaygın olarak kullanılmaktadır. Gelecekteki iklim senaryolarının etkisini simüle etmek için, uzun süreli ölçülenveriler yeterli olmadığında, ölçülen veriler hatalı kayıtlar içerdiğinde, gözlenen verilerin toplanması maliyetliya da çok zaman gerektirdiği için iklim modelleri kullanılmaktadır. İklim modellerinin çoğu rüzgar hızı, bağılnem, solar radyasyon, sıcaklık ve yağış gibi bir ya da daha fazla iklim değişkenini tahmin etmektedir.CLIGEN, USCLIMATE ve WGEN gibi iklim modelleri, standart normal dağılım kullanarak mak ve minsıcaklık değerleri oluşturmaktadır. Bu çalışmada Kayseri, Sivas ve Yozgat meteorolojik istasyonlarına ait uzunyıllar ortalama sıcaklık verileri CLIGEN iklim modeli ile simüle edilmiştir. Hem gözlenen hem de simüleedilen verilerin normal dağılıma uyumu Kolmogorv-Smirnov testi ile belirlenmiştir. Aylık gözlenen maksimumve minumum sıcaklıkların normal dağılıma uymadığı, çarpıklık değerinin hemen hemen tüm aylar için negatifolduğu görülmüştür. CLIGEN, yaz ayları için gözlenen değerin üzerinde simüle ettiği ve bazı aylar içinbulunan değerlerin normal dağılım gösterdiği görülmüştür.

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Cite This Article

AKDOĞAN, Y., DEMİR, S., ÖZKAYNAR, B. H. (2022). Statistical Evaluation of Predicted Maximum and Minimum Temperatures withCLIGEN Climate Model. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, Cilt: 37 - Sayı: 3, 190–201. https://ajindex.com/articles/520788

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References (10)

  1. 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.
  2. Bindraban, P. S., Coauthors., 2012. Assessing the İmpact of Soil Degradation on Food Production. Current Opinion in Environmental Sustainability, 4, 478–488.
  3. 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.
  4. Corder, G.W., Foreman, D.I., 2009. Nonparametric Statistics: an İntroduction. Nonparametric Stat. NonStatisticians 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.
  5. 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.,
  6. 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.
  7. Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea,D.W., Medina-Elizade,M. 2006. Global Temperature Change. PNAS. 103 (39):14288-14293.
  8. 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.
  9. 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
  10. Kumar, V., Jain, S.K.,