Skip to main content

Advertisement

SpringerLink
Log in

Historical variability and future changes in seasonal extreme temperature over Iran

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Daily minimum and maximum temperatures are provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (https://esg.pik-potsdam.de/search/isimip/).

Code availability

The R package used in this paper is available on GitHub (https://github.com/ECCC-CDAS/RClimDex)

References

  • Abbasian M, Moghim S, Abrishamchi A (2019) Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoret Appl Climatol 135(3–4):1465–1483

    Article  Google Scholar 

  • Ahmadi H, Baaghideh M, Dadashi-Roudbari A (2021) Climate change impacts on pistachio cultivation areas in Iran: a simulation analysis based on CORDEX-MENA multi-model ensembles. Theoret Appl Climatol 145(1):109–120

    Article  Google Scholar 

  • Ahmed, K., Sachindra, D. A., Shahid, S., Iqbal, Z., Nawaz, N., & Khan, N. (2020). Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research, 236, 104806.

  • Alexander LV, Arblaster JM (2017) Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Weather and Climate Extremes 15:34–56

    Article  Google Scholar 

  • Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., ... & Tagipour, A. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5).

  • Ali S, Eum HI, Cho J, Dan L, Khan F, Dairaku K, Fahad S (2019) Assessment of climate extremes in future projections downscaled by multiple statistical downscaling methods over Pakistan. Atmos Res 222:114–133

    Article  Google Scholar 

  • Anandhi A, Hutchinson S, Harrington J, Rahmani V, Kirkham MB, Rice CW (2016) Changes in spatial and temporal trends in wet, dry, warm and cold spell length or duration indices in Kansas, USA. Int J Climatol 36(12):4085–4101

    Article  Google Scholar 

  • Añel JA, Fernández-González M, Labandeira X, López-Otero X, De la Torre L (2017) Impact of cold waves and heat waves on the energy production sector. Atmosphere 8(11):209

    Article  Google Scholar 

  • Azarderakhsh, M., Prakash, S., Zhao, Y., & AghaKouchak, A. (2020). Satellite-based analysis of extreme land surface temperatures and diurnal variability across the hottest place on Earth. IEEE Geoscience and Remote Sensing Letters.

  • Bai, H., Xiao, D., Wang, B., Liu, D. L., Feng, P., & Tang, J. (2020). Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China Plain. International Journal of Climatology.

  • Barnett AG, Hajat S, Gasparrini A, Rocklöv J (2012) Cold and heat waves in the United States. Environ Res 112:218–224

    Article  Google Scholar 

  • Bindi M, Olesen JE (2011) The responses of agriculture in Europe to climate change. Reg Environ Change 11(1):151–158

    Article  Google Scholar 

  • Bishop CH, Abramowitz G (2013) Climate model dependence and the replicate Earth paradigm. Clim Dyn 41(3–4):885–900

    Article  Google Scholar 

  • Boucher, O. (2018). The Geoengineering Model Intercomparison Project–introduction to the second special issue.

  • Bozkurt D, Rojas M, Boisier JP, Rondanelli R, Garreaud R, Gallardo L (2019) Dynamical downscaling over the complex terrain of southwest South America: present climate conditions and added value analysis. Clim Dyn 53(11):6745–6767

    Article  Google Scholar 

  • Carvalho D, Rocha A, Gómez-Gesteira M, Santos CS (2017) Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections. Renewable Energy 101:29–40

    Article  Google Scholar 

  • Chen YD, Li J, Zhang Q (2016) Changes in site-scale temperature extremes over China during 2071–2100 in CMIP5 simulations. Journal of Geophysical Research: Atmospheres 121(6):2732–2749

    Article  Google Scholar 

  • Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Hinton, T., Jones, C. D., ... & Senior, C. (2008). Evaluation of the HadGEM2 model. Hadley Centre Technical Note HCTN 74, Met Office Hadley Centre, Exeter, UK.

  • Cook J, Nuccitelli D, Green SA, Richardson M, Winkler B, Painting R, Skuce A (2013) Quantifying the consensus on anthropogenic global warming in the scientific literature. Environmental research letters 8(2):024024

    Article  Google Scholar 

  • Darand M (2020) Future changes in temperature extremes in climate variability over Iran. Meteorological Applications 27(6):e1968

    Article  Google Scholar 

  • Darand M, Masoodian A, Nazaripour H, Daneshvar MM (2015) Spatial and temporal trend analysis of temperature extremes based on Iranian climatic database (1962–2004). Arab J Geosci 8(10):8469–8480

    Article  Google Scholar 

  • Diffenbaugh NS, Krupke CH, White MA, Alexander CE (2008) Global warming presents new challenges for maize pest management. Environmental Research Letters 3(4):044007

    Article  Google Scholar 

  • Donat MG, Alexander LV, Herold N, Dittus AJ (2016) Temperature and precipitation extremes in century-long gridded observations, reanalyses, and atmospheric model simulations. Journal of Geophysical Research: Atmospheres 121(19):11–174

    Google Scholar 

  • Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Hewitson B (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J Geophys Res: Atmos 118(5):2098–2118

    Article  Google Scholar 

  • Dong S, Xu Y, Zhou B, Shi Y (2015) Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China. Adv Atmos Sci 32(8):1077–1091

    Article  Google Scholar 

  • Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E, Krasting JP (2012) GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: Physical formulation and baseline simulation characteristics. J clim 25(19):6646–6665

    Article  Google Scholar 

  • Fallah-Ghalhari G, Shakeri F, Dadashi-Roudbari A (2019) Impacts of climate changes on the maximum and minimum temperature in Iran. Theoret Appl Climatol 138(3–4):1539–1562

    Article  Google Scholar 

  • Gao J, Sheshukov AY, Yen H, Douglas-Mankin KR, White MJ, Arnold JG (2019) Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. J Hydrol 568:551–561

    Article  Google Scholar 

  • Ghalhari GF, Roudbari AD (2018) An investigation on thermal patterns in Iran based on spatial autocorrelation. Theoret Appl Climatol 131(3):865–876

    Article  Google Scholar 

  • Heavens NG, Mahowald NM, Soreghan GS, Soreghan MJ, Shields CA (2015) A model-based evaluation of tropical climate in Pangaea during the late Palaeozoic icehouse. Palaeogeogr Palaeoclimatol Palaeoecol 425:109–127

    Article  Google Scholar 

  • Hong YIN, Ying SUN (2018) Characteristics of extreme temperature and precipitation in China in 2017 based on ETCCDI indices. Adv Clim Chang Res 9(4):218–226

    Google Scholar 

  • Im ES, Pal JS, Eltahir EA (2017) Deadly heat waves projected in the densely populated agricultural regions of South Asia. Science advances 3(8):e1603322

    Article  Google Scholar 

  • IPCC (2013). In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panelon Climate Change. Cambridge University Press, Cambridge https://doi.org/10.1017/CBO9781107415324.

  • Iversen T, Bentsen M, Bethke I, Debernard JB, Kirkevåg A, Seland Ø, Seierstad IA (2013) The Norwegian earth system model, NorESM1-M-part 2: Climate response and scenario projections. Geoscientific Model Development 6(2):389

    Article  Google Scholar 

  • Ju H, van der Velde M, Lin E, Xiong W, Li Y (2013) The impacts of climate change on agricultural production systems in China. Clim Change 120(1–2):313–324

    Article  Google Scholar 

  • Kamyar A, Yazdanpanah H, Movahedi S, Morimoto D (2020) Assessment of the impacts of climate change on agro-climatic indices in Iran. Theoret Appl Climatol 142(3):1359–1367

    Article  Google Scholar 

  • Katiraie-Boroujerdy PS, Akbari Asanjan A, Chavoshian A, Hsu KL, Sorooshian S (2019) Assessment of seven CMIP5 model precipitation extremes over Iran based on a satellite-based climate data set. Int J Climatol 39(8):3505–3522

    Article  Google Scholar 

  • Kent ST, McClure LA, Zaitchik BF, Smith TT, Gohlke JM (2014) Heat waves and health outcomes in Alabama (USA): the importance of heat wave definition. Environ Health Perspect 122(2):151–158

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change 119(2):345–357

    Article  Google Scholar 

  • Kim YH, Min SK, Zhang X, Sillmann J, Sandstad M (2020) Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather and Climate Extremes 29:100269

    Article  Google Scholar 

  • Knutti R, Sedláček J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3(4):369–373

    Article  Google Scholar 

  • Kumar D, Kodra E, Ganguly AR (2014) Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Clim Dyn 43(9–10):2491–2518

    Article  Google Scholar 

  • Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset.

  • Lu C, Sun Y, Zhang X (2018) Multimodel detection and attribution of changes in warm and cold spell durations. Environ Res Lett 13(7):074013

    Article  Google Scholar 

  • Luo N, Guo Y, Gao Z, Chen K, Chou J (2020) Assessment of CMIP6 and CMIP5 model performance for extreme temperature in China. Atmos Ocean Sci Lett 13(6):589–597

    Article  Google Scholar 

  • Manzanas R, Gutiérrez JM, Fernández J, Van Meijgaard E, Calmanti S, Magariño ME, Herrera S (2018) Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications. Clim Service 9:44–56

    Article  Google Scholar 

  • Maraun D (2016) Bias correcting climate change simulations-a critical review. Curr Clim Change Reports 2(4):211–220

    Article  Google Scholar 

  • Merlone A, Al-Dashti H, Faisal N, Cerveny RS, AlSarmi S, Bessemoulin P, Rahimzadeh F (2019) Temperature extreme records: World Meteorological Organization metrological and meteorological evaluation of the C observations in Mitribah, Kuwait and Turbat, Pakistan in 2016/2017. Int J Climatol 39(13):5154–5169

    Article  Google Scholar 

  • Min SK, Zhang X, Zwiers F, Shiogama H, Tung YS, Wehner M (2013) Multimodel detection and attribution of extreme temperature changes. J Clim 26(19):7430–7451

    Article  Google Scholar 

  • Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Meehl GA (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756

    Article  Google Scholar 

  • Mushore TD, Mutanga O, Odindi J, Dube T (2018) Determining extreme heat vulnerability of Harare Metropolitan City using multispectral remote sensing and socio-economic data. J Spat Sci 63(1):173–191

    Article  Google Scholar 

  • Naderi M (2020) Extreme climate events under global warming in northern Fars Province, southern Iran. Theoret Appl Climatol 142(3):1221–1243

    Article  Google Scholar 

  • Navarro-Racines C, Tarapues J, Thornton P, Jarvis A, Ramirez-Villegas J (2020) High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Scientific Data 7(1):1–14

    Article  Google Scholar 

  • Noor M, bin Ismail T, Shahid S, Ahmed K, Chung ES, Nawaz N (2019) Selection of CMIP5 multi-model ensemble for the projection of spatial and temporal variability of rainfall in peninsular Malaysia. Theoret Appl Climatol 138(1–2):999–1012

    Article  Google Scholar 

  • Ntoumos A, Hadjinicolaou P, Zittis G, Lelieveld J (2020) Updated Assessment of Temperature Extremes over the Middle East-North Africa (MENA) Region from Observational and CMIP5 Data. Atmos 11(8):813

    Article  Google Scholar 

  • Ongoma V, Chen H, Gao C, Nyongesa AM, Polong F (2018) Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble. Nat Hazards 90(2):901–920

    Article  Google Scholar 

  • Pepin N, Bradley RS, Diaz HF, Baraër M, Caceres EB, Forsythe N, Miller JR (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5(5):424–430

    Article  Google Scholar 

  • Peterson TC, Heim RR Jr, Hirsch R, Kaiser DP, Brooks H, Diffenbaugh NS, Katz RW (2013) Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: state of knowledge. Bull Am Meteor Soc 94(6):821–834

    Article  Google Scholar 

  • Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Fang J (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51

    Article  Google Scholar 

  • Piticar A, Croitoru AE, Ciupertea FA, Harpa GV (2018) Recent changes in heat waves and cold waves detected based on excess heat factor and excess cold factor in Romania. Int J Climatol 38(4):1777–1793

    Article  Google Scholar 

  • Rahimi, J., Laux, P., & Khalili, A. (2020). Assessment of climate change over Iran: CMIP5 results and their presentation in terms of Köppen–Geiger climate zones. Theoretical and Applied Climatology, 1–17.

  • Rahimi M, Hejabi S (2018) Spatial and temporal analysis of trends in extreme temperature indices in Iran over the period 1960–2014. Int J Climatol 38(1):272–282

    Article  Google Scholar 

  • Rahimzadeh F, Asgari A, Fattahi E (2009) Variability of extreme temperature and precipitation in Iran during recent decades. Int J Climatol: A J Royal Meteorologic Soc 29(3):329–343

    Article  Google Scholar 

  • Raziei T, Mofidi A, Santos JA, Bordi I (2012) Spatial patterns and regimes of daily precipitation in Iran in relation to large-scale atmospheric circulation. Int J Climatol 32(8):1226–1237

    Article  Google Scholar 

  • Reyers M, Moemken J, Pinto JG (2016) Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble. Int J Climatol 36(2):783–796

    Article  Google Scholar 

  • Riahi K, Grübler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Chang 74(7):887–935

    Article  Google Scholar 

  • Roshan G, Nastos PT (2018) Assessment of extreme heat stress probabilities in Iran’s urban settlements, using first order Markov chain model. Sustain Cities Soc 36:302–310

    Article  Google Scholar 

  • Schär C (2016) Climate extremes: The worst heat waves to come. Nat Clim Chang 6(2):128–129

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389

    Article  Google Scholar 

  • Sharafati A, Pezeshki E (2020) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theoret Appl Climatol 139(1–2):389–402

    Article  Google Scholar 

  • Sheridan SC, Lee CC, Smith ET (2020) A comparison between station observations and reanalysis data in the identification of extreme temperature events. Geophysical Research Letters 47(15):e2020GL088120

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble Part 2 Future climate projections. J Geophys Res: Atmos 118(6):2473–2493

    Article  Google Scholar 

  • Soltani M, Laux P, Kunstmann H, Stan K, Sohrabi MM, Molanejad M, Zawar-Reza P (2016) Assessment of climate variations in temperature and precipitation extreme events over Iran. Theoret Appl Climatol 126(3–4):775–795

    Article  Google Scholar 

  • Stephenson DB, Diaz HF, Murnane RJ (2008) Definition, diagnosis, and origin of extreme weather and climate events. Clim Extremes Soc 340:11–23

    Article  Google Scholar 

  • Strelcová, K., Matyas, C., Kleidon, A., Lapin, M., Matejka, F., Blazenec, M., ... & Holecy, J. (Eds.). (2008). Bioclimatology and natural hazards. Springer Science & Business Media.

  • Suparta W, Yatim ANM (2017) An analysis of heat wave trends using heat index in East Malaysia. In Journal of Physics: Conference Series 852(1):012005

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. JGeophys Res: Atmos 106(D7):7183–7192

    Article  Google Scholar 

  • Tegegne G, Melesse AM (2020) Multimodel ensemble projection of hydro-climatic extremes for climate change impact assessment on water resources. Water Resour Manage 34(9):3019–3035

    Article  Google Scholar 

  • Tegegne G, Melesse AM, Worqlul AW (2020) Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Science of the Total Environment 704:135357

    Article  Google Scholar 

  • Theil H (1950) A rank invariant method of linear and polynomial regression analysis, part 3. Netherlands Akademie Van Wettenschappen Proceedings 53:1397–1412

    Google Scholar 

  • Thornton PK, Ericksen PJ, Herrero M, Challinor AJ (2014) Climate variability and vulnerability to climate change: a review. Glob Change Biol 20(11):3313–3328

    Article  Google Scholar 

  • Ullah S, You Q, Zhang Y, Bhatti AS, Ullah W, Hagan DFT, Ali A (2020) Evaluation of CMIP5 models and projected changes in temperatures over South Asia under global warming of 1.5 oC, 2 oC, and 3 oC. Atmospheric Research 246:105122

    Article  Google Scholar 

  • Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9(1):1–11

    Google Scholar 

  • Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Rose SK (2011) The representative concentration pathways: an overview. Clim Change 109(1):5–31

    Article  Google Scholar 

  • Vuille M, Franquist E, Garreaud R, Lavado Casimiro WS, Cáceres B (2015) Impact of the global warming hiatus on Andean temperature. J Geophys Res: Atmos 120(9):3745–3757

    Article  Google Scholar 

  • Wang B, Lee JY, Xiang B (2015) Asian summer monsoon rainfall predictability: a predictable mode analysis. Clim Dyn 44(1):61–74

    Article  Google Scholar 

  • Wang, M., Lau, W. K., & Wang, J. (2021). Impact of middle east dust on subseasonal-to-seasonal variability of the Asian summer monsoon. Climate Dynamics, 1–18.

  • Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Ise T (2011) MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci Model Develop 4(4):845

    Article  Google Scholar 

  • Weedon GP, Balsamo G, Bellouin N, Gomes S, Best MJ, Viterbo P (2014) The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514

    Article  Google Scholar 

  • Wise M, Calvin K, Thomson A, Clarke L, Bond-Lamberty B, Sands R, Edmonds J (2009) Implications of limiting CO2 concentrations for land use and energy. Sci 324(5931):1183–1186

    Article  Google Scholar 

  • World Meteorological Organization, 2010. No. 1055. 978–92–63–11055–8 (Switzerland). www.wmo.int. Accessed 20 May 2021

  • Wuebbles D, Goodman A, Janssen E, Lee H, Olsen SC, Meehl G, Santer B (2014) CMIP5 climate model analyses: climate extremes in the United States. Bull Am Meteor Soc 95(4):571–583

    Article  Google Scholar 

  • Xu C, Chen Y, Yang Y, Hao X, Shen Y (2010) Hydrology and water resources variation and its response to regional climate change in Xinjiang. J Geog Sci 20(4):599–612

    Article  Google Scholar 

  • Xu K, Wu C, Hu BX (2019) Projected changes of temperature extremes over nine major basins in China based on the CMIP5 multimodel ensembles. Stoch Env Res Risk Assess 33(1):321–339

    Article  Google Scholar 

  • Xu L, Wang A (2019) Application of the bias correction and spatial downscaling algorithm on the temperature extremes from CMIP5 multimodel ensembles in China. Earth and Space Sci 6(12):2508–2524

    Article  Google Scholar 

  • Xu Z, Han Y, Yang Z (2019) Dynamical downscaling of regional climate: A review of methods and limitations. Sci China Earth Sci 62(2):365–375

    Article  Google Scholar 

  • Yang Y, Tang J, Xiong Z, Wang S, Yuan J (2019) An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations. Clim Dyn 53(7–8):4629–4649

    Article  Google Scholar 

  • Ying H, Zhang H, Sun Y, Zhao J, Zhang Z, Guo X, Deng G (2020) CMIP5-Based Spatiotemporal Changes of Extreme Temperature Events during 2021–2100 in Mainland China. Sustainability 12(11):4418

    Article  Google Scholar 

  • You Q, Jiang Z, Wang D, Pepin N, Kang S (2018) Simulation of temperature extremes in the Tibetan Plateau from CMIP5 models and comparison with gridded observations. Clim Dyn 51(1–2):355–369

    Article  Google Scholar 

  • Yu B, Lin H, Kharin VV, Wang XL (2020) Interannual variability of North American winter temperature extremes and its associated circulation anomalies in observations and CMIP5 simulations. J Clim 33(3):847–865

    Article  Google Scholar 

  • Zamani R, Berndtsson R (2019) Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method. Theoret Appl Climatol 137(1–2):533–543

    Article  Google Scholar 

  • Zamani Y, Monfared SAH, Hamidianpour M (2020) A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran. Theoret Appl Climatol 142(3):1613–1623

    Article  Google Scholar 

  • Zarrin A, Dadashi Roudbari A (2020) Projection the Long-Term Outlook Iran Future Temperature Based on the Output of The coupled model intercomparison project phase 6 (CMIP6). J Earth and Space Physics 46(3):583–602. https://doi.org/10.22059/jesphys.2020.304870.1007226

    Article  Google Scholar 

  • Zarrin A, Dadashi-Roudbari A (2021) Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoret Appl Climatol 144(1):643–660

    Article  Google Scholar 

  • Zarrin A, Ghaemi H, Azadi M, Farajzadeh M (2010) The spatial pattern of summertime subtropical anticyclones over Asia and Africa: A climatological review. Int J Climatol: A J Royal Meteorologic Soc 30(2):159–173

    Google Scholar 

  • Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246, 105111.

  • Zhang H, Zhou G, Li Liu D, Wang B, Xiao D, He L (2019) Climate-associated rice yield change in the Northeast China Plain: A simulation analysis based on CMIP5 multi-model ensemble projection. Sci Total Environ 666:126–138

    Article  Google Scholar 

  • Zhang X, Zwiers FW, Hegerl G (2009) The influences of data precision on the calculation of temperature percentile indices. Int J Climatol: A J Royal Meteorologic Soc 29(3):321–327

    Article  Google Scholar 

  • Zhu J, Huang DQ, Yan PW, Huang Y, Kuang XY (2017) Can reanalysis datasets describe the persistent temperature and precipitation extremes over China? Theoret Appl Climatol 130(1–2):655–671

    Article  Google Scholar 

  • Zhu, X., Wei, Z., Dong, W., Ji, Z., Wen, X., Zheng, Z., ... & Chen, D. (2020). Dynamical downscaling simulation and projection for mean and extreme temperature and precipitation over central Asia. Climate Dynamics, 1–28.

  • Zwiers FW, Zhang X, Feng Y (2011) Anthropogenic influence on long return period daily temperature extremes at regional scales. J Clim 24(3):881–892

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the Iran Meteorological Organization (IRIMO) for providing the necessary data and information. We also acknowledge the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and associated World Climate Research Program (WCRP) for the production of the data.

Funding

This research was funded by the Vice Chancellor for Research of Ferdowsi University of Mashhad, which is hereby acknowledged.

Author information

Authors and Affiliations

  1. Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

    Azar Zarrin, Abbasali Dadashi-Roudbari & Samira Hassani

Authors
  1. Azar Zarrin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbasali Dadashi-Roudbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samira Hassani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceived and designed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari.

Collected the data: Azar Zarrin, Abbasali Dadashi-Roudbari, and Samira Hassani.

Contributed data or analysis tools: Azar Zarrin, Abbasali Dadashi-Roudbari, and Samira Hassani.

Performed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari.

Wrote the paper: Azar Zarrin and Abbasali Dadashi-Roudbari.

Writing—review and editing: Azar Zarrin.

Corresponding author

Correspondence to Azar Zarrin.

Ethics declarations

Ethics approval

We approve the ethical responsibilities of authors.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarrin, A., Dadashi-Roudbari, A. & Hassani, S. Historical variability and future changes in seasonal extreme temperature over Iran. Theor Appl Climatol 146, 1227–1248 (2021). https://doi.org/10.1007/s00704-021-03795-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-021-03795-7

Keywords

Search

Navigation