Comparison of statistical downscaling in climate change models to simulate climate elements in Northwest Iran

Document Type : Full length article

Authors

1 Associate professor of Physical Geography, University of Mohaghegh Ardabili, Iran

2 PhD student in Physical Geography, University of Mohaghegh Ardabili, Iran

3 Assistant professor of climatology, Climate Research Center Mashhad, Iran

Abstract

Introduction
Linking global climate models with local scale as a micro climatic process is of great importance. Recently, many attempts have been made by  researchers to develop dynamics and statistical downscaling methods for expressing climate change at a local and regional scale. Two general techniques are used for downscaling of the output of General Circulation Models (GCM). The primary solution is application of statistical methods in which the output of a statistical model (MOS) and a planned approach to weather short-term numerical prediction is presented. The second is regional climate model (RCM), that same is limited GCM model in a subnet of the network global model and by dynamic method using climatic conditions temporal changes according to GCM model. Both methods play an important role in determination of the potential effects of climate change caused by increased greenhouse gas emissions. Much work is done to use this method for downscaling of the global model output in different areas in which the performance of the model is assessed and uncertainty analysis has been done on these methods or were compared by other statistical methods.
 
 Materials and methods
In this study, three approaches to statistical downscaling methods are provided. The first approach uses random generation of climate models based on time series and fourier series delivers. LARS-WG statistical model  is one of the ways to build this approach. In this model, the empirical distribution of daily series of dry and wet precipitation and solar radiation is desirable. The minimum and maximum daily temperatures are the daily stochastic process with mean and standard deviations. Seasonal cycles by means of finite fourier series have the order 3 and model residuals (model errors) is approximated by a normal distribution.
The second approach is regression model or transfer function that is used to examine the relationship between atmospheric parameters and synoptic factors (predictor variables) to have a vision of the future (Instant predictor variable) for a transfer function. One of the applications that combine these two approaches is based on statistical downscaling model (SDSM). The meteorological station data are required as input and output in seven steps of GCM model are downscaled on the basis of daily data in the area. 
The third downscaling model is Artificial Neural Network (ANN). This model is a non-linear regression type in which a relationship is developed between a few selected large-scale atmospheric predictors and basin scale meteorological predictors. In developing that relationship a time lagged recurrent network is used in which the inputs are supplied through tap delay line and the network is trained using a variation of backpropagation algorithm. A slightly different approach is application of the predictors for the case of neural network downscaling.
To compare the data generated by the models and observation values, we  employed two non-parametric tests of MANN-Whitney. For the observed values and the model values, we have also used Spearman correlation. The basic correlation analysis is based on linear correlation coefficient of the two variables. One of the important indicators that can be used for performance evaluation model, index model mean square error (LARS-WG) is defined as follows:
 
 
 
The North West area of Iran, including the provinces of East Azerbaijan and West Azerbaijan, Ardabil, Zanjan and a part of Kurdistan in the geographical coordinates ˚49 '30 ˚44 '07 and the ˚36 '00 to ˚39 '50 North. To study the effects of climate change in the region, we used statistical models for a minimum period of 1961-1990. In addition to the complete statistical period, synoptic meteorological stations of old climate data confirmed the country's Meteorological Agency to help some regional stations for multi-year statistical vacuum.
 
Results and discussion
The results indicated that according to the MANN-Whitney test the performance of three models for minimum temperature in the study area are close together. Spearman correlation test results for minimum temperature show that the number of correlation, in all stations for LARS-WG model is less than the other two. This demonstrates low performance of LARS-WG model in this respect. The average number of months with significant correlation for ANN model with seven months of the year represented that the best performance was among the three models. SDSM model with a four-month correlation table is in the middle. In terms of LARS-WG index for the minimum temperature, LARS-WG and ANN models have average values close together. This shows the error of sum of squares closer to the two models. LARS-WG values are less than the SDSM model and this shows the SDSM model is less accurate than the other two models.
According to our evaluation, according to MANN-Whitney test of the model generated values it can be stated that the difference between the observed and tested model values, for minimum and maximum temperatures in three models have not different performance. But the results were somewhat different in different stations. Correlation data for SDSM and ANN models for maximum high temperature and minimum temperature for solidarity in SDSM model is less than ANN model. However, because the same structure prediction methods and large-scale use of such an outcome was not unexpected.
MANN-Whitney test for precipitation results show that significant differences between the observed and modeled data for ANN is much more than the other two. This reflects the low performance of this model. SDSM and LARS-WG models have similar good performance in this regard. The Spearman correlation test indicated that all three models have a low correlation. This represents that the three models are low in this respect in the study area. According to the LARS-WG, the SDSM model is better than the other two models in average performance.
 

Keywords

Main Subjects


آشفته، پ.‌س. و مساح بوانی، ع. (1388). تأثیر عدم قطعیت تغییر اقلیم بر رژیم سیلاب (مطالعة موردی: حوضة آیدوغموش آذربایجان شرقی)، مجلة تحقیقات منابع آب ایران، 5(2).
اشرف، ب.؛ موسوی بایگی، م.؛ کمالی، غ. و داوری، ک. (1390). پیش‌بینی ‌تغییرات فصلی پارامترهای اقلیمی در 20 سال آتی با استفاده از ریزمقیاس‌نمایی آماری داده‌های مدل HADCM3) مطالعة موردی: استان خراسان رضوی)، نشریة آب و خاک (علوم و صنایع کشاورزی)، 25(۴): ۹۴۵ـ 957.
بابائیان، ا. و کوهی، م. (1391). ارزیابی شاخص‌های اقلیم کشاورزی تحت سناریوهای تغییر اقلیم در ایستگاه‌های منتخب خراسان رضوی، نشریة آب و خاک (علوم و صنایع کشاورزی)، 26(4): 953ـ 967.
بابائیان، ا.؛ نجفی نیک، ز.؛ زابل عباسی، ف.؛ حبیبی نوخندان، م.؛ ادب، ح. و ملبوسی، ش. (1388). ارزیابی تغییر اقلیم کشور در دورة 2039ـ 2010 میلادی با استفاده از ریزمقیاس‌نمایی داده‌های مدل گردش عمومی جو ECHO-، مجلة جغرافیا و توسعه، 16: 152ـ 135.
خانی تملیه، ذ. (1391). ارزیابی اثرات تغییر اقلیم بر شاخص‌های خشک‌سالی در سیستم‌های منابع آب با استفاده از تکنیک تولید دادة مصنوعی جریان (مطالعة موردی: دریاچة ارومیه). پایان‌نامة گروه مهندسی آب، دانشکدة کشاورزی، دانشگاه ارومیه.
خورشیددوست، ع.م. و قویدل، ی. (1384). شبیه‌سازی آثار دو برابر شدن دی‌اکسید کربن جو بر تغییر اقلیم تبریز با استفاده از مدل آزمایشگاه پویایی سیالات ژئوفیزیکی (GFDL)، مجلة محیط‌شناسی، 39: 1ـ 10.
دلاور، م.؛ بابایی، ا. و فتاحی، ا. (1393). بررسی اثرات تغییر اقلیم بر نوسانات تراز آب دریاچة ارومیه، نشریة پژوهش‌های اقلیم‌شناسی، 5(19 و 20).
دهقانی‌پور، ا.ح.؛ حسن‌زاده، م.ج.؛ عطاری، ج. و عراقی‌نژاد، ش. (1390). ارزیابی توانمندی مدل SDSM در ریزمقیاس‌نمایی بارش، دما، و تبخیر (مطالعة موردی: ایستگاه سینوپتیک تبریز)، یازدهمین سمینار سراسری آبیاری و کاهش تبخیر، 18 ـ 20 بهمن‌ماه 1390.
رسولی ع.ا.؛ رضایی بنفشه، م.؛ مساح بوانی؛ ع.؛ خورشید‌دوست؛ ع.م. و قرمزچشمه، ب. (1393). بررسی اثر عوامل مرفو- اقلیمی بر دقت ریزمقیاس‌گردانی مدل LARS-WG، نشریة علوم و مهندسی آبخیزداری ایران، 8(24): 9 ـ 18.
رضایی زمان، م.؛ مرید، س. و دلاور، م. (1392). ارزیابی اثرات تغییر اقلیم بر متغیرهای هیدروکلیماتولوژی حوضة سیمینه‌رود، نشریة آب و خاک، 27(6): 1247ـ 1259.
سبحانی، ب.؛ اصلاحی، م. و بابائیان، ا. (1394). کارایی الگوهای ریزمقیاس‌نمایی آماری SDSM و LARS-WG در شبیه‌سازی متغیرهای هواشناسی در حوضۀ آبریز دریاچۀ ارومیه، پژوهش‌هایجغرافیایطبیعی، 47(4):516.
شهرآشوب، م. و میکائیلی، ف. (1367). مفاهیم و روش‌های آماری، مرکز نشر دانشگاهی.
قمقامی، م.؛ قهرمان، ن. و حجابی، س. (1393). آشکارسازی تأثیر تغییر اقلیم بر خشک‌سالی‌های هواشناسی در شمال غرب ایران، مجلة فیزیک زمین و فضا، 40(1): 167- 184.
گل‌محمدی، م. و مساح بوانی، ع.ض. (1390). بررسی تغییرات شدت و دورة بازگشت خشک‌سالی حوضة قره‌سو در دوره‌‌های آتی تحت تأثیر تغییر اقلیم، نشریة آب و خاک (علوم و صنایع کشاورزی)، 25(۲): ۳۱۵ـ 326.
مهسافر، ح.؛ مکنون، ر. و ثقفیان، ب. (1389). اثر تغییر اقلیم بر بیلان آبی دریاچة ارومیه، مجلة تحقیقات منابع آب ایران، 7(1).
Ashofteh, P.S. and Masahboani, A. (2009). Impact of Climate Change Uncertainty on Flood regime (Case study: East Azarbaijan Aidehghomoush Basin), Iranian Journal of Water Resources Research, 5(2).
Ashraf, B.; Mousavibaygi, M.; Kamali, G. and Davari, K. (2011). Predict seasonal changes of climatology parameters in next 20 years by using statistical downscaling of HADCM3 model output (case study: Korasan Razavi Province), Journal of Water and Soil (Science and Industrial Agriculture), 25(4): 945-957.
Babaeian, A. and Kouhi, M. (2012). Indexes evaluation of agriculture climate under climate change scenarios at selected stations in Khorasan Razavi, Journal of Water and Soil (Science and Industrial Agriculture), 26(4): 953-967.
Babaeian, A.; Najafinik, Z.; Habibinokhandan, M.; Zabolabbasi, F.; Adab, H. and Malbousi, S. (2007). The modeling of Iran climate in the period 2010-2039, by using statistical downscaling of ECHO-G model output, Technical workshop on climate change impacts on water resources management, 13 Feb 2007.
Cheema, S.B.; Rasul, G.; Ali, G. and Kazmi, D.H. (2013). A Comparison of Minimum Temperature Trends with Model Projections, Pakistan Journal of Meteorology, 8(15).
Coulibaly, P.; Dibike, Y.B. and Anctil, F. (2005). Downscalingprecipitation and temperature with temporal neural networks, J.Hydrometeorol.
Dehghanipoor, A.H.; Hasanzadeh, M.J.; Attari, J. And IraqiNezhad, Sh. (2011). Evaluation of SDSM model capability in the Downscaling of precipitation, temperature, and evaporation (Case Study: Synoptic Station of Tabriz), 11th Irrigation Seminar and Evaporation Reduction, 18-20 February 2011.
Delaware, M.; Babaei, A. and Fattahi, A. (2014). Investigating the effects of climate change on the fluctuation of water balance in Urmia Lake, Journal of Clinical Research, 5(19 and 20).
Fowler, H.J; Blenkinsop, S. and Tebaldi, C. (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, International Journal of Climatology, 27:1547-1578.
Golmohammadi, M. and Masahboani, A. (2011). Evaluation of drought severity and recurrence in future periods affected by climate change in the basin Gharehsou, Journal of Water and Soil (Science and Industrial Agriculture), 25(2): 315-326.
Goodarzi, E.; Dastorani, M.T.; Massah Bavani, A. and Talebi, A. (2015). Evaluation of the Change-Factor and LARS-WG Methods of Downscaling for Simulation of Climatic Variables in the Future (Case study: Herat Azam Watershed, Yazd - Iran), ECOPERSIA, 3(1).
Hashemi, M.Z.; Shamseldin, A.Y. and Melville, B.W. (2009). Downscaling of future rainfall extreme events: a weather generator based approach, 18th World IMACS/ MODSIM Congress, Cairns, Australia 13-17 July 2009.
Karamouz, M.; Fallahi, M.; Nazif, S. and RahimiFarahan, M. (2009). Long Lead Rainfall Prediction Using Statistical Downscaling and Arti_cial Neural Network Modeling, Transaction A: Civil Engineering, 16(2): 165-172.
Kazmi, D.H.; Rasul, G.; Li, J. and Cheema, S.B. (2014). Comparative Study for ECHAM5 and SDSM in Downscaling Temperature for a Geo-Climatically Diversified Region, Pakistan, Applied Mathematics, 5: 137-143.
Khani Tamilie, D. (2012). Assessment of the effects of climate change on drought indices in water resources systems using artificial flow generation method (Case study: Urmia lake), Department of Water Engineering, Faculty of Agriculture, Urmia University.
Korshiddoust, A.M. and Ghavidel, Y. (2005). Simulation of the effects of doubling of atmospheric carbon dioxide on climate change in Tabriz using the GFDL model, Journal of the Environment, 39: 1-10.
Mahsafar, H.; Maknoun, R. and Saghafian, B. (2010). The Effect of Climate Change on Water Burning Urmia Lake, Journal of Iranian Water Resources Research, 7(1).
Meenu, R.; Rehana, S. and Mujumdar, P.P. (2012). Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM, Hydrological Processes, Published online in Wiley Online Library, DOI: 10.1002/hyp.9220.
Nury, A.H. and Alam, M.J.B. (2014). Performance Study of Global Circulation Model HADCM3 Using SDSM for Temperature and Rainfall in North-Eastern Bangladesh, Journal Of Scientific Research, 6(1): 87-96.
Osman, Y.; Al‐Ansari, N.; Abdellatif, M.; Aljawad, S.B. and Knutsson, S. (2014). Expected Future Precipitation in Central Iraq using LARSWG Stochastic Weather Generator, Published Online 2014 in SciRes. http://www.scirp.org/journal/eng http://dx.doi.org/10.4236/eng.2014.
Principle, J.C.; Euliano, N.R. and Lefebvre, W.C. (2000). Neural and Adaptive Systems: Fundamentals through Simulations, Wiley, New York.
Qamghami, M.; Ghahreman, N. And Hejabi, S. (2014). Detection of Climate Change Effects on Meteorological Droughts in Northwest of Iran, Journal of Physics of Earth and Space, 40(1): 167-184.
Rajabi, A. and Shabanlou, S. (2012). Climate Index Changes In Future By Using SDSM In Kermanshah, Iran, Journal of Environmental Research And Development, 7(1).
Rasouli, A.A.; Rezaeibanafsheh, M.; Masahboani, A.; Khorshiddoust, A.M. and Ghermezcheshmeh, B. (2014). Study Of Morpho-Climatic Factors Effect On The Accuracy Of Downscaling Of LARS-WG Model, Iranian Journal of Watershed Management Science and Engineering, 8(24): 9-18.
Reddy, K.S.; Kumar, M.; Maruthi, V.; Umesha, B. Vijayalaxmi and NageswarRao, C.V.K. (2014). Climate change analysis in southern Telangana region, Andhra Pradesh using LARS-WG model, Research Articles, Current Science, 107(1): 54-62.
Resko, P.; Szeidl, L. and Semenov, M.A. (1991). A serial approach to local stochastic models, J. Ecological Modeling, 57: 27-41.
Rezaei-e-Zaman, M.; Morid, S. And Delaware, M. (2013). Evaluation of Climate Change Effects on Hydroclimatic Variables in Siminehrood Hidro Basin, Journal of Water and Soil, 27(6): 1247-1259.
SajjadKhan, M.; Coulibaly, P. and Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods, Journal of Hydrology, 319: 357-382.
Sarwar, R.; Irwin, S.E.; King, L.M. And Simonovic, S.P. (2010). Assessment of climatic vulnerability in the Upper Thames River basin: Downscaling with SDSM, Water Resources Research Report, Department of Civil and Environmental Engineering, The University of Western Ontario.
Sharashoub, M. and Mikaeili, F. (1989). Concepts and Statistical Methods, Markeze Nashre Daneshgahi.
Sobhani, B.; Eslahi, M. and Babaeian, I. (2015). Performance of Statistical Downscaling Models of SDSM and LARS-WG in the Simulation of Meteorological Parameters in the Basin of Lake Urmia, Physical Geography Research Quarterly, 47(4): 499-516.
Wilby, R.L.; Dawson, C.W. and Barrow, E.M. (2002). A decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17: 147-159.
Wilby, R.L.; Hay, L.E. and Leavesley, G.H. (1999). A comparison ofdownscaled and raw GCM output: implications for climatechange scenarios in the San Juan River Basin, Colorado. JHydrol, 225: 67-91.
Wilby, R.L.; S.P. Zorita, E.; Timbal, B.; Whetton, P. and Mearns, L. (2004). Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, IPCC Reports.
Wilby, R.L.; Tomlinson, O.J. And Dawson, C.W. (2003). Multi-Site Simulation Of Precipitation By Conditional Resampling, Climate Research, 23: 183-194.
Zhaofei, L.; Zongxue, X.; Charles, S.P.; Guobin, F. and Liu, L. (2011). Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China, Int. J. Climatol, 31: 2006-2020.