[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method

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

Abstract

GCMs (general circulation models) are main tools for generating climate projections for climate change research in hydrology and water resources. Accordingly, evaluating the performance of these models in simulating future climate is very important for choice of proper models. In this study, performance of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) model series was assessed using a technique for order performance by similarity to ideal solution (TOPSIS)-based approach together with normalized root mean square error (NRMSE), the Taylor skill score (STaylor), and two probability density function (PDF) skill scores. Precipitation and temperature data during 1976 to 2005 from three river basins including Zard River (ZR), Bakhtegan (BKH), and Ghareso (GH) in west and southwest Iran were used to select the best model. In general, models showed superiority in simulating temperature over precipitation. Based on the GCM ranking results for the ZR Basin, MIROC-ESM and IPSL-CM5A-LR were selected as the best and the weakest model, respectively. For the BKH Basin, the best model was BCC-CSM1.1 and the weakest IPSL-CM5A-MR and CCSM4. In other words, BCC-CSM1.1 had the maximum relative closeness to ideal solution. Based on the TOPSIS results, BCC-CSM1.1 and CanESM2 were the best models and IPSL-CM5A-MR the weakest model with a minimum relative closeness to the ideal solution in simulating temperature and precipitation for the GH basin. The approach presented in this study can be utilized to select appropriate climate models in other regions for future studies of climate change.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Ahmadalipour A, Rana A, Moradkhani H, Sharma A (2017) Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis. Theor Appl Climatol 128(1–2):71:78

    Google Scholar 

  • Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78:1–3

    Article  Google Scholar 

  • Brouziyne Y, Abouabdillah A, Hirich A, Bouabid R, Zaaboul R, Benaabidate L (2018) Modeling sustainable adaptation strategies toward a climate-smart agriculture in a Mediterranean watershed under projected climate change scenarios. Agric Syst 162:154–163

    Article  Google Scholar 

  • Brown JR, Power SB, Delage FP, Colman RA, Moise AF, Murphy BF (2011) Evaluation of the South Pacific Convergence Zone in IPCC AR4 climate model simulations of the twentieth century. J Clim 24:1565–1582

    Article  Google Scholar 

  • Chen L, Yu Y, Sun D-Z (2013) Cloud and water vapor feedbacks to the El Niño warming: are they still biased in CMIP5 models? J Clim 26:4947–4961

    Article  Google Scholar 

  • Choudhury D, Sharma A, Sen Gupta A, Mehrotra R, Sivakumar B (2016) Sampling biases in CMIP5 decadal forecasts. J Geophys Res Atmos 121:3435–3445

    Article  Google Scholar 

  • Donner LJ et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24:3484–3519

    Article  Google Scholar 

  • Dufresne J-L, Foujols M-A, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, de Noblet N, Duvel JP, Ethé C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix JY, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre MP, Lefevre F, Levy C, Li ZX, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40:2123–2165

    Article  Google Scholar 

  • Dunne JP et al (2012) GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: Physical formulation and baseline simulation characteristics. Journal of Climate 25.19:6646–6665

  • Errasti I, Ezcurra A, Sáenz J, Ibarra-Berastegi G (2011) Validation of IPCC AR4 models over the Iberian Peninsula. Theor Appl Climatol 103:61–79

    Article  Google Scholar 

  • Franklin CN, Sun Z, Bi D, Dix M, Yan H, Bodas-Salcedo A (2013) Evaluation of clouds in access using the satellite simulator package cosp: global, seasonal, and regional cloud properties. J Geophys Res Atmos 118:732–748. https://doi.org/10.1029/2012JD018469

    Article  Google Scholar 

  • Fu G, Liu Z, Charles SP, Xu Z, Yao Z (2013) A score-based method for assessing the performance of GCMs: a case study of southeastern Australia. J Geophys Res Atmos 118:4154–4167

    Article  Google Scholar 

  • Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang M (2011) The community climate system model version 4. J Clim 24:4973–4991

    Article  Google Scholar 

  • Grose MR, Brown JN, Narsey S, Brown JR, Murphy BF, Langlais C, Gupta AS, Moise AF, Irving DB (2014) Assessment of the CMIP5 global climate model simulations of the western tropical Pacific climate system and comparison to CMIP3. Int J Climatol 34:3382–3399

    Article  Google Scholar 

  • Hirota N, Takayabu YN (2013) Reproducibility of precipitation distribution over the tropical oceans in CMIP5 multi-climate models compared to CMIP3. Clim Dyn 41:2909–2920

    Article  Google Scholar 

  • Hung M-P, Lin J-L, Wang W, Kim D, Shinoda T, Weaver SJ (2013) MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J Clim 26:6185–6214

    Article  Google Scholar 

  • Hwang CL, Yoon K (1981) Methods for multiple attribute decision making. In: Multiple attribute decision making. Lecture notes in economics and mathematical systems, vol 186. Springer, Berlin, Heidelberg

  • Iversen T, Bentsen M, Bethke I, Debernard JB, Kirkevåg A, Seland Ø, Drange H, Kristjánsson JE, Medhaug I, Sand M, Seierstad IA (2012) The Norwegian Earth System Model, NorESM1-M – part 2: climate response and scenario projections. Geosci Model Dev Discuss 5:2933–2998. https://doi.org/10.5194/gmdd-5-2933-2012

    Article  Google Scholar 

  • Keellings D (2016) Evaluation of downscaled CMIP5 model skill in simulating daily maximum temperature over the southeastern United States. Int J Climatol 36(12):4172–4180

    Article  Google Scholar 

  • Koutroulis AG, Grillakis MG, Tsanis IK, Papadimitriou L (2016) Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments. Climate Dynamics, 47(5-6):1881-1898

  • Lee JK, Kim YO (2017) Selection of representative GCM scenarios preserving uncertainties. J Water Clim Change 8(4):641–651. https://doi.org/10.2166/wcc.2017.101https://doi.org/10.2166/wcc.2017.101

  • Meehl GA, Washington WM, Arblaster JM, Hu A, Teng H, Tebaldi C, Sanderson BN, Lamarque JF, Conley A, Strand WG, White JB III (2012) Climate system response to external forcings and climate change projections in CCSM4. J Clim 25:3661–3683

    Article  Google Scholar 

  • Mi ZF, Wei YM, He CQ et al (2017) Regional efforts to mitigate climate change in China: a multi-criteria assessment approach. Mitig Adapt Strateg Glob Change 22(1):45:66

    Article  Google Scholar 

  • Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region. Clim Dyn 43:2663–2680

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Reichler T, Kim J (2008) How well do coupled models simulate today’s climate? Am Meteorol Soc 89(3):303–312

    Article  Google Scholar 

  • Rotstayn LD, Jeffrey SJ, Collier MA, Dravitzki SM, Hirst AC, Syktus JI, Wong KK (2012) Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations. Atmos Chem Phys 12:6377–6404. https://doi.org/10.5194/acp-12-6377-2012

    Article  Google Scholar 

  • Srinivasa Raju K, Nagesh Kumar D (2015) Ranking general circulation models for India using TOPSIS. J Water Clim Chang 6(2):288–299

    Article  Google Scholar 

  • Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) Atmospheric component of the MPI-M earth system model: ECHAM6. J Adv Model Earth Syst 5(2):146–172.

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Thrasher B, Nemani R (2012) NASA earth exchange global daily downscaled projections (NEX-GDDP) assessed at: https://cds.nccs.nasa.gov/nex-gddp

  • Voldoire A, Sanchez-Gomez E, Mélia DS et al (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121

    Article  Google Scholar 

  • Volodin EM, Dianskii NA, Gusev AV (2010) Simulating present-day climate with the INMCM4. 0 coupled model of the atmospheric and oceanic general circulations. Izv Atmos Ocean Phys 46(4):414–431

    Article  Google Scholar 

  • von Salzen K, Scinocca JF, McFarlane NA et al (2013) The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: physical processes. Atmosphere-Ocean 51:104–125

    Article  Google Scholar 

  • Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J Clim 23:6312–6335

    Article  Google Scholar 

  • Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S, Kawamiya M (2011) MIROC-ESM 2010: model description and basic results of CMIP 5-20c3m experiments. Geosci Model Dev 4:845–872

    Article  Google Scholar 

  • Wei T, Yang S, Moore JC, Shi P, Cui X, Duan Q, Xu B, Dai Y, Yuan W, Wei X, Yang Z, Wen T, Teng F, Gao Y, Chou J, Yan X, Wei Z, Guo Y, Jiang Y, Gao X, Wang K, Zheng X, Ren F, Lv S, Yu Y, Liu B, Luo Y, Li W, Ji D, Feng J, Wu Q, Cheng H, He J, Fu C, Ye D, Xu G, Dong W (2012) Developed and developing world responsibilities for historical climate change and CO2 mitigation. Proc Natl Acad Sci 109:12911–12915

    Article  Google Scholar 

  • Wójcik R (2015) Reliability of CMIP5 GCM simulations in reproducing atmospheric circulation over Europe and the North Atlantic: a statistical downscaling perspective. Int J Climatol 35:714–732

    Article  Google Scholar 

  • Wu T, Li W, Ji J, Xin X, Li L, Wang Z, Zhang Y, Li J, Zhang F, Wei M, Shi X, Wu F, Zhang L, Chu M, Jie W, Liu Y, Wang F, Liu X, Li Q, Dong M, Liang X, Gao Y, Zhang J (2013) Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century. J Geophys Res Atmos 118:4326–4347

    Article  Google Scholar 

  • Yin L, Fu R, Shevliakova E, Dickinson RE (2013) How well can CMIP5 simulate precipitation and its controlling processes over tropical South America? Clim Dyn 41:3127–3143

    Article  Google Scholar 

  • Yukimoto S, Adachi Y, Hosaka M et al (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3—model description and basic performance. J Meteorol Soc Jpn 90(A):23–64

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Water Resources Research Center, Shahrekord University, Shahrekord, Iran

    Reza Zamani

  2. Division of Water Resources Engineering & Center for Middle Eastern Studies, Lund University, Box 118, SE-221 00, Lund, Sweden

    Ronny Berndtsson

Authors
  1. Reza Zamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ronny Berndtsson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Zamani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zamani, R., Berndtsson, R. Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method. Theor Appl Climatol 137, 533–543 (2019). https://doi.org/10.1007/s00704-018-2616-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-018-2616-0