Abstract
General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov statistic (KS), Sen’s slope estimator, and the Taylor diagram are used for the evaluation. GCMs are ranked based on each statistic at seasonal and annual time scales. Results show that most GCMs perform reasonably well in simulating the annual and seasonal temperature over Iran. The majority of the GCMs have a poor skill to simulate precipitation, particularly at seasonal scale. Based on the results, the best GCMs to represent temperature and precipitation simulations over Iran are the CMCC-CMS (Euro-Mediterranean Center on Climate Change) and the MRI-CGCM3 (Meteorological Research Institute), respectively. The results are valuable for climate and hydrometeorological studies and can help water resources planners and managers to choose the proper GCM based on their criteria.
Similar content being viewed by others
References
Abbasnia M, Toros H (2016) Future changes in maximum temperature using the statistical downscaling model (SDSM) at selected stations of Iran. Model Earth Syst Environ 2:68. https://doi.org/10.1007/s40808-016-0112-z
Abbaspour KC, Faramarzi M, Ghasemi SS, Yang H (2009) Assessing the impact of climate change on water resources in Iran. Water Resour Res 45:W10434. https://doi.org/10.1029/2008wr007615
Aloysius NR, Sheffield J, Saiers JE, Li H, Wood EF (2016) Evaluation of historical and future simulations of precipitation and temperature in Central Africa from CMIP5 climate models. J Geophys Res Atmos 121:130–152. https://doi.org/10.1002/2015jd023656
Argüeso D, Evans JP, Fita L (2013) Precipitation bias correction of very high resolution regional climate models. Hydrol Earth Syst Sci 17:4379–4388. https://doi.org/10.5194/hess-17-4379-2013
Belda M, Holtanová E, Halenka T, Kalvová J, Hlávka Z (2015) Evaluation of CMIP5 present climate simulations using the Köppen-Trewartha climate classification. Clim Res 64:201–212. https://doi.org/10.3354/cr01316
Bonsal BR, Prowse TD (2006) Regional assessment of GCM-simulated current climate over Northern Canada. Arctic 59:15–128. https://doi.org/10.14430/arctic335
Chen L, Frauenfeld OW (2014) A comprehensive evaluation of precipitation simulations over China based on CMIP5 multimodel ensemble projections. J Geophys Res Atmos 119:5767–5786. https://doi.org/10.1002/2013jd021190
Dessai S (2005) Limited sensitivity analysis of regional climate change probabilities for the 21st century. J Geophys Res Atmos 110:D19108. https://doi.org/10.1029/2005jd005919
Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res Atmos 113:D06104. https://doi.org/10.1029/2007jd008972
Gohari A, Eslamian S, Abedi-Koupaei J, Massah Bavani A, Wang D, Madani K (2013) Climate change impacts on crop production in Iran’s Zayandeh-Rud River Basin. Sci Total Environ 442:405–419. https://doi.org/10.1016/j.scitotenv.2012.10.029
Hannah L (2015) Climate change biology. Elsevier, Amsterdam
Hao Z, AghaKouchak A, Phillips TJ (2013) Changes in concurrent monthly precipitation and temperature extremes. Environ Res Lett 8:034014. https://doi.org/10.1088/1748-9326/8/3/034014
Harris I, Jones P, Osborn T, Lister D (2013) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Climatol 34:623–642. https://doi.org/10.1002/joc.3711
Hashemi H, Uvo CB, Berndtsson R (2015) Coupled modeling approach to assess climate change impacts on groundwater recharge and adaptation in arid areas. Hydrol Earth Syst Sci 19:4165–4181. https://doi.org/10.5194/hess-19-4165-2015
IPCC (1996) Climate Change 1995: Impacts, adaptations, and mitigation of climate change: scientific-technical analyses. Contribution of Working Group II to the second Assessment Report of the Intergovernmental Panel on Climate Change [Watson RT, Zinyowera MC, Moss RH (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA
IPCC (2007) Climate models and their evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA
IPCC (2013) Summary for policymakers. In: Climate Change 2013: The Physical Sciences Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker TF, Qin, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley BM (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA
Johnson F, Sharma A (2009) Measurement of GCM skill in predicting variables relevant for hydroclimatological assessments. J Clim 22:4373–4382. https://doi.org/10.1175/2009jcli2681.1
Kharin VV, Zwiers FW, Zhang X, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC Ensemble of Global Coupled Model Simulations. J Clim 20:1419–1444. https://doi.org/10.1175/jcli4066.1
Khazaei MR, Zahabiyoun B, Saghafian B (2011) Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model. Int J Climatol 32:1997–2006. https://doi.org/10.1002/joc.2416
Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199. https://doi.org/10.1002/grl.50256
Kolmogorov AN (1933) Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari 4:83–91
Loukas A, Vasiliades L, Tzabiras J (2008) Climate change effects on drought severity. Adv Geosci 17:23–29. https://doi.org/10.5194/adgeo-17-23-2008
McMahon TA, Peel MC, Karoly DJ (2015) Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation. Hydrol Earth Syst Sci 19:361–377. https://doi.org/10.5194/hess-19-361-2015
Miao C, Duan Q, Sun Q, Huang Y, Kong D, Yang T, Ye A, di Z, Gong W (2014) Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia. Environ Res Lett 9:055007. https://doi.org/10.1088/1748-9326/9/5/055007
Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nasrollahi N, AghaKouchak A, Cheng L, Damberg L, Phillips T, Miao C et al (2015) How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts? Water Resour Res 51:2847–2864. https://doi.org/10.1002/2014wr016318
Nazemosadat MJ, Ravan V, Kahya E, Ghaedamini H (2016) Projection of temperature and precipitation in southern Iran using ECHAM5 simulations. Iran J Sci Technol Trans A Sci 40:39–49. https://doi.org/10.1007/s40995-016-0009-8
Pascale S, Lucarini V, Feng X, Porporato A, Hasson SU (2014) Analysis of rainfall seasonality from observations and climate models. Clim Dyn 44:3281–3301. https://doi.org/10.1007/s00382-014-2278-2
Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376. https://doi.org/10.1175/jcli4253.1
Pincus R, Batstone CP, Hofmann RJP, Taylor KE, Glecker PJ (2008) Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J Geophys Res 113:D14209. https://doi.org/10.1029/2007JD00933
Razmara P, Massah Bavani AR, Motiee H, Torabi S, Lotfi S (2013) Investigating uncertainty of climate change effect on entering runoff to Urmia Lake Iran. Hydrol Earth Syst Sci Discuss 10:2183–2214. https://doi.org/10.5194/hessd-10-2183-2013
Reichler T, Kim J (2008) Supplement to how well do coupled models simulate Today’s climate? Bull Am Meteorol Soc 89:S1–S6. https://doi.org/10.1175/bams-89-3-reichler
Reifen C, Toumi R (2009) Climate projections: past performance no guarantee of future skill? Geophys Res Lett 36:L13704. https://doi.org/10.1029/2009gl038082
Samadi S, Carbone GJ, Mahdavi M, Sharifi F, Bihamta MR (2012) Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment. Hydrol Earth Syst Sci Discuss 9:4869–4918. https://doi.org/10.5194/hessd-9-4869-2012
Samadi S, Wilson CA, Moradkhani H (2013) Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model. Theor Appl Climatol 114:673–690. https://doi.org/10.1007/s00704-013-0844-x
Sayari N, Bannayan M, Alizadeh A, Farid A (2012) Using drought indices to assess climate change impacts on drought conditions in the northeast of Iran (case study: Kashafrood basin). Meteorol Appl 20:115–127. https://doi.org/10.1002/met.1347
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Shadkam S, Ludwig F, Van Vliet MT, Pastor A, Kabat P (2016) Preserving the world second largest hypersaline lake under future irrigation and climate change. Sci Total Environ 559:317–325. https://doi.org/10.1016/j.scitotenv.2016.03.190
Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1716–1733. https://doi.org/10.1002/jgrd.50203
Smirnov NV (1933) Estimate of deviation between empirical distribution functions in two independent sample (in Russian). Bull Moscow Univ 2:3–16
Sonali P, Kumar DN, Nanjundiah RS (2016) Intercomparison of CMIP5 and CMIP3 simulations of the 20th century maximum and minimum temperatures over India and detection of climatic trends. Theor Appl Climatol 128:465–489. https://doi.org/10.1007/s00704-015-1716-3
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000jd900719
Taylor KE, Stouffer RJ, Meehl GA (2009) A summary of the CMIP5 experiment design. PCDMI Rep., 33 pp. [Available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf]
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. https://doi.org/10.1175/bams-d-11-00094.1
Wang L, Chen W (2013) A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int J Climatol 34:2059–2078. https://doi.org/10.1002/joc.3822
Weigel AP, Liniger MA, Appenzeller C (2008) Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q J R Meteorol Soc 134:241–260. https://doi.org/10.1002/qj.210
Yin L, Fu R, Shevliakova E, Dickinson RE (2012) How well can CMIP5 simulate precipitation and its controlling processes over tropical South America? Clim Dyn 41:3127–3143. https://doi.org/10.1007/s00382-012-1582-y
Zarghami M, Abdi A, Babaeian I, Hassanzadeh Y, Kanani R (2011) Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob Planet Chang 78:137–146. https://doi.org/10.1016/j.gloplacha.2011.06.003
Acknowledgements
The authors would like to thank the CMIP5 climate modeling groups (Table 1) and the Climatic Research (CRU) from the University of East Anglia for making their products publicly available. We recognize the UNESCO Chair in Water and Environment Management for Sustainable Cities, Sharif University of Technology for the help in this work. We are thankful to the reviewer whose comments and suggestions improved the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Abbasian, M., Moghim, S. & Abrishamchi, A. Performance of the general circulation models in simulating temperature and precipitation over Iran. Theor Appl Climatol 135, 1465–1483 (2019). https://doi.org/10.1007/s00704-018-2456-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-018-2456-y