Modeling and spatial analysis of future needs for cooling in Iran

Document Type : Full length article


1 Professor of climatology, Yazd University, Yazd, Iran

2 PhD candidate in climate hazards, Yazd University, Yazd, Iran

3 PhD candidate in urban climatology, Shahid Beheshti University, Faculty of Earth Science, Iran


Climate changes and global warming as important issues in environmental sciences have attracted the attention of many researchers.  In the study of the climate and atmospheric changes, it is essential to understand the temporal conditions of climate parameters, particularly degree day.  The temperature as one of the most critical climate parameter is effective in the global warming. Thus, a variety of indices and methods have been presented for the analysis of this parameter by many researchers. Degree day is one of the important indices. The studies about the climate change and energy requirements are necessary in Iran, in that, the increasing needs of the society due to the energy crisis of the world, fossil fuel resources, and increased temperature lead to a new state of energy in the near future of this country. In the present research, we have attempted to detect cooling degree days of the country using spatial statistics. 
Materials and methods
To examine the impacts of global warming on Cooling Degree Days (CDD), we have used the average of the daily temperature data derived from EH5OM database. The EH5OM is an Atmospheric Ocean Global Circulation Model (AOGCM) of the fifth general circulation models of atmosphere with spectral dynamic core. The model was presented by Max Plank physics institute. In this research, we have used the data in a period from 2015-2050 under scenario A1B. The reason we have selected this scenario is the ability to have equal use of fossil and non-fossil resources of the future. For a spatio-temporal exploration of the degree day, the balance of cooling need of Iran has been considered at 23.9° C threshold. The threshold was applied by the institute of US standard of science. We have employed three methods in ArcGIS10.3 for these analyses. We have also used Global Moran I to evaluate the spatial autocorrelation the cooling needs in the future decades, Anselin Local Morans I to draw the clusters and non-clusters, and Getis Ord Gi statistic to analyze the spatial patterns of the cooling needs. 
Results and discussion
The cooling degree day needs has a positive autocorrelation in the future (α=0.01). This has confirmed dependency of the cooling need for Iran. With the beginning of summer, the Global Moran I is reached 0.9. The changing patterns of the index in different months of the year have given distribution for all the country. In winter times the cooling need is reduced over the country. The parameter is at the peak in April, May, and June. The results have showed the spatial and temporal patterns of the needs for cooling in different regions of Iran. In the winter times, the need for cooling the buildings shows a proportional reduction in all the country. The cold climatic conditions in January and February reduced the need for cooling the house environment. The cooling needs in different areas of Lut, Zabol, and Turkmen regions have indicated the effects of elevation on the temperature. The southern coasts of Chabahar and Hormozgan also observed the temperature higher than average. In Marth and September the country can be categorized into three groups; the coastal plains, mountain areas, and internal arid plains. These regions have different patterns in different months. The southern coasts of Iran and coastal plains of north Iran will have the highest needs for cooling in the future decades. The mountainous areas have the lowest cooling needs in the country. In the mountain areas of Iran the need gradient is reduced towards the central low lands.
The results of spatial autocorrelation using local Moran model for Iran have indicated that the cooling need for the future decades follow a spatial pattern. The LISA has also indicated that the most needs for cooling is in the period from April to September. Thus, the southern areas of Iran and the mountainous areas have the highest and the lowest needs for cooling. The three regions of coastal plains, mountainous areas, and central arid plain will have different spatial patterns of cooling needs in different decades in the period of this study. The results of the study on the difference of cooling needs in two categories of flat and relief terrain areas are consistent with the results of Masoudian et al. (2011). In the future, the patterns may have changes in different latitudes. The highest need for cooling will be occurred in Chabahar and Hormozgan coasts.


Main Subjects

امیدوار، ک.؛ ابراهیمی، ر.؛ داداشی رودباری، ع. و ملک‏میرزایی، م. (1394). واکاوی زمانی- مکانی فرین‏های سرد ایران تحت تأثیر گرمایش جهانی به منظور کاهش مخاطرات، دانشمخاطرات، 2(4): 423-437.
انتظاری، ع.؛ داداشی رودباری، ع. و اسدی، م. (1394). ارزیابی خودهمبستگی تغییرات زمان، شمارة مکانی جزایر گرمایی در خراسان رضوی، جغرافیاومخاطراتمحیطی، 16: 125-146.
داداشی رودباری، ع. (1394). ارزیابی سیل‏خیزی با استفاده از مدل ریاضی HEC-HMS، تحلیل‏های آماری، و GIS در حوضة آبخیز هراز، پایان‏نامة کارشناسی ارشد، دانشکدة جغرافیا و علوم محیطی دانشگاه حکیم سبزواری، سبزوار.
شمسی‏پور، ع. (1393). مدلسازیآبوهوایینظریهوروش، چ 2، تهران: انتشارات دانشگاه تهران.
علی‏آبادی، ک. و داداشی رودباری، ع. (1394). بررسی تغییرات الگوهای خودهمبستگی فضایی دمای بیشینة ایران، مطالعاتجغرافیاییمناطقخشک، 6(21): 86-104.
فلاح قالهری، غ.؛ اسدی، م. و داداشی رودباری، ع. (1394). تحلیل فضایی پراکنش رطوبت در ایران، پژوهشهایجغرافیایطبیعی، 47(4): 637-650.
کرمی، م. و داداشی رودباری، ع. (1393). ارزیابی الگوهای بارشی استان خراسان رضوی با استفاده از روش‏های نوین‏آمار فضایی، مجلة علمی‏- ترویجی سامانهوسطوحآبگیرباران، 4(3): 61-72.
مسعودیان، س. (1383). بررسی روند دمای ایران در نیم سدة گذشته، مجلة جغرافیاوتوسعه، 2: 89-106.
حلیمی برده زرد، م. (1390). بررسی تأثیر تغییر اقلیم بر مصرف انرژی برق بخش خانگی ایران، پایان‏نامة کارشناسی ارشد دانشگاه تربیت مدرس، به راهنمایی دکتر منوچهر فرج‏زاده اصل.
مسعودیان، س.ا؛ علیجانی، ب. و ابراهیمی، ر. (1390). واکاوی میانگین درجه/ روز مورد نیاز (گرمایش و سرمایش) در قلمرو ایران، پژوهشنامةجغرافیایی، 1: 23-36.
مسعودیان، س.ا؛ ابراهیمی، ر. و محمدی، م. (1393 الف). پهنه‏بندی مکانی- زمانی نیاز گرمایش و سرمایش فصلی و سالانة ایران، فصل‏نامة علمی- پژوهشی اطلاعاتجغرافیاییسپهر، 23(90): 83-90.
مسعودیان، ا.؛ ابراهیمی، ر. و یاراحمدی، ا. (1393 ب). واکاوی مکانی- زمانی میزان روند ماهانة درجة روز گرمایش در قلمرو ایران‏زمین، مجلة جغرافیاوتوسعةناحیهای، 12(10).
Aliabadi, K. and Dadashi Roudbari, A. (2015). Assessing changes patterns of spatial autocorrelation of maximum temperature of Iran, Arid Regions Geographic Studies, 6(21): 86-104. [In Persian].
Anselin, L.; Syabri, I. and Kho, Y. (2009). GeoDa: an introduction to spatial data analysis. In Fischer MM, Getis A (Eds) Handbook of applied spatial analysis, Springer, Berlin, Heidelberg and New York, pp.73-89.
Arakawa, A., & Schubert, W. H. (1974). Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. Journal of the Atmospheric Sciences, 31(3), 674-701.
Casper, J.K. (2010). Global warming cycles: ice ages and glacial retreat, Infobase Publishing.
Castañeda, M.E. and Claus, F. (2013). Variability and trends of heating degree‐days in Argentina, International Journal of Climatology, 33(10): 2352-2361.
Christenson, M.; Manz, H. and Gyalistras, D. (2006). Climate warming impact on degree-days and building energy demand in Switzerland, Energy Conversion and Management, 47(6): 671-686.
Dadashi Roudbari, A. (2015). Assessment of flooding using a mathematical model HEC-HMS, statistical analysis and GIS in watershed Haraz, Master's Thesis, Hakim Sabzevari University, Sabzevar. [In Persian].
De Rosa, M.; Bianco, V.; Scarpa, F. and Tagliafico, L.A. (2014). Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach, Applied energy, 128: 217-229.
Elguindi, N.; Bi, X.; Giorgi, F.; Nagarajan, B.; Pal, J.; Solmon, F. ... and Zakey, A. (2010). RegCM Version 4.0 User’s Guide. Trieste, Italy.
Emanuel, K. A. (1991). A scheme for representing cumulus convection in large-scale models. Journal of the Atmospheric Sciences, 48(21), 2313-2329.
Entezari, A.; Dadashiroudbari, A. and Asadi, M. (2016). Assessment of spatial autocorrelation of spatial-temporal alteration of temperature heat islands in Khorasan Razavi province, Geography and Environmental Hazards, 16: 125-146. [In Persian].
Fallah Ghalhari, G.; Asadi, M. and Dadashi Roudbari, A. (2016). Spatial Analysis of Humidity Propagation over Iran, Physical Geography Research Quarterly, 47(4): 637-650. [In Persian].
Fallah Ghalhari, G.; Dadashi Roudbari, A. and  Asadi, M. (2016). Identifying the spatial and temporal distribution characteristics of precipitation in Iran, Arabian Journal of Geosciences, 9(12): 1-12. doi: 10.1007/s12517-016-2606-4
Frank, T. (2005). Climate change impacts on building heating and cooling energy demand in Switzerland, Energy and buildings, 37(11): 1175-1185.
Giorgi, F.; Coppola, E.; Solmon, F.; Mariotti, L.; Sylla, M.B.; Bi, X. ... and Turuncoglu, U.U. (2012). RegCM4: model description and preliminary tests over multiple CORDEX domains, Climate Research, 52: 7-29.
Halimi Bardeh Zard, M. (2011). The effect of climate change on the household sector's energy consumption around Iran, Master's Thesis, Tarbiat Modares University, Tehran. [In Persian].
Handbook, A.S.H.R.A.E. (2009). ASHRAE handbook–fundamentals, Atlanta, GA.
Holtslag, A. A. M., De Bruijn, E. I. F., & Pan, H. L. (1990). A high resolution air mass transformation model for short-range weather forecasting. Monthly Weather Review, 118(8), 1561-1575.
IPCC (Intergovernmental Panel on Climate Change) (2001) Impacts, adaptation, and vulnerability climate change 2001, Third Assessment Report of the IPCC. University Press, Cambridge.
Jiang, F.; Li, X.; Wei, B.; Hu, R. and Li, Z. (2009). Observed trends of heating and cooling degree-days in Xinjiang Province, China, Theoretical and applied climatology, 97(3-4): 349-360.
Karami, M. and Dadashi Roudbari, A. (2015). Evaluation of Rain Patterns in Khorasan Razavi Province Using Spatial Statistical Modern Methods, Iranian Journal of Rainwater Catchment Systems, 4(3): 61-72. [In Persian].
Massodian, M.A. (2004). Evaluating the trend of Iran temperature in the past half century, Geography and Developmant Iranian Journal, 2: 89-106. [In Persian].
Massodian, M.A.; Alijani, B. and Ebrahimi, R. (2011). Spatiotemporal analysis of the monthly heating degree days Trend in the territory of Iran, Journal Management system, 1: 23-36. [In Persian].
Massodian, M.A.; Ebrahimi, R. and Mohammadi, M. (2014). Analysis of the mean Degree/ day required (heating and cooling) in the territory of Iran, 23(90): 83-90. [In Persian].
Massodian, M.A.; Ebrahimi, R. and Yarahmadi, E. (2015). Spatiotemporal Analysis of the Monthly Heating Degree Days in Iran, Journal of Geography and Regional Development, 12(23): 111-127. [In Persian].
NEELIN, J. (2010). Climate Change and Climate Modeling, Cambridge University Press the Edinburgh Building, Cambridge CB2 8RU, UK.
Omidvar, K.; Ebrahimi, R.; Dadashiroudbari, A. and Malekmirzaie, M. (2015). Analyzed the effects of global warming on the occurrence of in Iran the extreme cold temperatures, Enviromental Hazards Management, 2(4): 423-437. [In Persian].
Pal, J. S., Eltahir, E. A., & Small, E. E. (2000). Simulation of regional-scale water and energy budgets- Representation of subgrid cloud and precipitation processes within RegCM. Journal of Geophysical Research, 105(D24), 29579-29594.
Reichler, T. and  Kim, J. (2008). How well do coupled models simulate today's climate?, Bulletin of the American Meteorological Society, 89(3): 303.
Roeckner, E.; Brokopf, R.; Esch, M.; Giorgetta, M.; Hagemann, S.; Kornblueh, L.; Manzini, E.; Schlese, U; Schulzweida, U. (2006). Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model, J. Clim, 19: 3771-3791.
Rogerson, P.A., (2006), Statistics Methods for Geographers: students Guide, SAGE Publications. Los Angeles, California.
Roshan, G.R. and Grab, S.W. (2012). Regional climate change scenarios and their impacts on water requirements for wheat production in Iran, Int J Plant Prod, 6(2): 239-266.
Scott, L.M. and Janikas, M.V. (2010). Spatial statistics in ArcGIS, In Handbook of applied spatial analysis (pp. 27-41), Springer Berlin Heidelberg.
Semmler, T.; McGrath, R.; Steele‐Dunne, S.; Hanafin, J.; Nolan, P. and Wang, S. (2010). Influence of climate change on heating and cooling energy demand in Ireland, International Journal of Climatology, 30(10): 1502-1511.
Shamsipour, A. (2014). Climate Modeling (Theory and Method). 2nd Edition, University of Tehran Press, tehran.298p. [In Persian].
Wang, H. and Chen, Q. (2014). Impact of climate change heating and cooling energy use in buildings in the United States, Energy and Buildings, 82: 428-436.
Wang, X.; Chen, D. and Ren, Z. (2010). Assessment of climate change impact on residential building heating and cooling energy requirement in Australia, Building and Environment, 45(7): 1663-1682.
Volume 49, Issue 2
July 2017
Pages 283-299
  • Receive Date: 26 March 2016
  • Revise Date: 20 November 2016
  • Accept Date: 23 November 2016
  • First Publish Date: 22 June 2017