Skip to main content

Advertisement

Log in

Intercomparison of CMIP5 and CMIP3 simulations of the 20th century maximum and minimum temperatures over India and detection of climatic trends

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

Abstract

Climate change impact assessment has become one of the most important subjects of the research community because of the recent increase in frequency of extreme events and changes in the spatiotemporal patterns of climate. This paper analyses the ability of 46 coupled climate models from Coupled Model Intercomparison Project phases 3 and 5 (CMIP5 and CMIP3). The performance of each climate model was assessed based on its skills in simulating the current seasonal cycles (monthly) of both maximum temperature and minimum temperature (Tmax, Tmin) over India. The performance measures such as coefficient of correlation (Skill_r), root mean square error (Skill_rmse), and the skill in simulating the observed probability density function (Skill_s) are mainly employed for evaluation of the simulated monthly seasonal cycle. A new metric called Skill_All which is an intersection of the above three metrics has been defined for the first time. A notable enhancement of Skill_All for CMIP5 vis-a-vis CMIP3 is observed. Further, three best CMIP5 models each for Tmax and Tmin were selected. The methodology employed in this study for model assessment is implemented for the first time for India, which establishes a robust foundation for the climate impact assessment study. The seasonal trends in Tmax and Tmin were analyzed over all the temperature homogenous regions of India for different time slots during the 20th century. Significant trends in Tmin can be seen during most of the seasons over the entire Indian region during last four decades. This establishes the signature of climate change over most parts of India.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Anandhi A, Nanjundiah RS (2014) Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores. Theor Appl Climatol 1–16. doi:10.1007/s00704-013-1043-5

  • Arnell NW, Reynard NS (1996) The effects of climate change due to global warming on river flows in Great Britain. J Hydro 183:397–424

    Article  Google Scholar 

  • Boccolari M, Malmusi S (2013) Changes in temperature and precipitation extremes observed in Modena, Italy. Atmos Res 122:16–31. doi:10.1016/j.atmosres.2012.10.022

    Article  Google Scholar 

  • Bonfils C, Duffy PB, Lobell DB (2006) Comments on methodology and results of calculating central California surface temperature trends: evidence of human-induced climate change. J Clim 20:4486–4489. doi:10.1175/JCLI4247.1

    Article  Google Scholar 

  • Carter TR, Jones PD, Hulme M, New M (2004) A comprehensive set of high-resolution grid points of monthly climate for Europe and the globe: the observed record (1901– 2000) and 16 scenarios (2001–2100) Tyndall Working Paper 55, Tyndall Centre, University of East Anglia, Norwich, United Kingdom [Available online at http://www.tyndallac.uk/]

  • Chiew FHS, Teng J, Vaze J, Kirono DGC (2009) Influence of global climate model selection on runoff impact assessment. J Hydrol 379:172–180. doi:10.1016/j.jhydrol.2009.10.004

    Article  Google Scholar 

  • Coumou D, Robinson A, Rahmstorf S (2013) Global increase in record-breaking monthly-mean temperatures. Clim Chang 118:771–782. doi:10.1007/s10584-012-0668-1

    Article  Google Scholar 

  • Dessai S, Lu X, Hulme M (2005) Limited sensitivity analysis of regional climate change probabilities for the 21st century. J Geophys Res 110:D19108. doi:10.1029/2005JD005919

    Article  Google Scholar 

  • Diallo I, Sylla MB, Camara M, Gaye AT (2013) Interannual variability of rainfall over the Sahel based on multiple regional climate models simulations. Theor Appl Climatol 113:351–362. doi:10.1007/s00704-012-0791-y

    Article  Google Scholar 

  • Diffenbaugh NS, Giorgi F (2012) Climate change hotspots in the CMIP5 global climate model ensemble. Clim Chang 114:813–822. doi:10.1007/s10584-012-0570-x

    Article  Google Scholar 

  • Douville H, Ribes A, Decharme B, Alkama R, Sheffield J (2013) Anthropogenic influence on multi decadal changes in reconstructed global evapotranspiration. Nat Clim Change 3:59–62. doi:10.1038/NCLIMATE1632

    Article  Google Scholar 

  • Driscoll S, Bozzo A, Gray LJ, Robock A, Stenchikov G (2012) Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions. J Geophys Res 117:D17105. doi:10.1029/2012JD017607

    Article  Google Scholar 

  • Errasti I, Ezcurra A, Sáenz J, Berastegi GI (2011) Validation of IPCC AR4 models over the Iberian Peninsula. Theor Appl Climatol 103:61–79. doi:10.1007/s00704-010-0282-y

    Article  Google Scholar 

  • Fall S, Watts A, Nielsen-Gammon J, Jones E, Niyogi D, Christy JR, Pielke RA Sr (2011) Analysis of the impacts of station exposure on the US Historical Climatology Network temperatures and temperature trends. J Geophys Res 116:D14120. doi:10.1029/2010JD015146

    Article  Google Scholar 

  • Fang F, Guo J, Sun L, Wang J, Wang X (2014) The effects of urbanization on temperature trends in different economic periods and geographical environments in northwestern China. Theor Appl Climatol 116:227–241. doi:10.1007/s00704-013-0944-7

    Article  Google Scholar 

  • Feng S, Hu Q, Huang W, Ho CH, Li R, Tang Z (2014) Projected climate regime shift under future global warming from multimodel, multi-scenario CMIP5 simulations. Glob Planet Chang 112:41–52. doi:10.1016/j.gloplacha.2013.11.002

    Article  Google Scholar 

  • Hamed KH, Rao AR (1998) A modified Mann–Kendall trend test for autocorrelated data. J Hydrol 204:219–246

    Article  Google Scholar 

  • Helfer F, Lemckert C, Zhang H (2012) Impacts of climate change on temperature and evaporation from a large reservoir in Australia. J Hydrol 475:365–378. doi:10.1016/j.jhydrol.2012.10.008

    Article  Google Scholar 

  • Huo Z, Dai X, Feng S, Kang S, Huang G (2013) Guanhua Huang effect of climate change on reference evapotranspiration and aridity index in arid region of China. J Hydrol doi:10.1016/j.jhydrol.2013.04.011

  • IPCC (2001) The scientific basis: Third Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, UK

  • IPCC (2007) The physical science basis: Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK

  • IPCC (2014) Climate change 2013: The physical science basis: Working Group I Contribution to the IPCC Fifth Assessment Report. Cambridge University Press, Cambridge, UK

  • Johnson FM, Sharma A (2009) Measurement of GCM skill in predicting variables relevant for hydroclimatological assessments. J Clim 22:4373–4382. doi:10.1175/2009JCLI2681.1

    Article  Google Scholar 

  • Johnson FM, Sharma A (2010) Comparison of Australian open water body evaporation trends for current and future climates estimated from class a evaporation pans and general circulation models. J Hydrometeor 11:105–121. doi:10.1175/2009JHM1158.1

    Article  Google Scholar 

  • Johnson F, Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour Res 48:W01504. doi:10.1029/2011WR010464

    Google Scholar 

  • Johnson F, Westra S, Sharma A, Pitman AJ (2011) An assessment of GCM skill in simulating persistence across multiple time scales. J Clim 1924:3609–3623. doi:10.1175/2011JCLI3732.1

    Article  Google Scholar 

  • Jung W, Moradkhani H, Chang H (2012) Uncertainty assessment of climate change impacts for hydrologically distinct river basins. J Hydrol 466:73–87. doi:10.1016/j.jhydrol.2012.08.002

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation methods. Charless Griffin, London

    Google Scholar 

  • Khaliq MN, Ouarda TBMJ, Gachon P, Sushama L, St.-Hilaire A (2009) Identification of hydrologic trends in the presence of serial and cross correlations a review of selected methods and their application to annual flow regimes of Canadian rivers. J Hydrol 368:117–130. doi:10.1016/j.jhydrol.2009.01.035

    Article  Google Scholar 

  • Knutti R (2008) Should we believe model predictions of future climate change? Philos Trans Roy Soc London, A 366:4647–4664. doi:10.1098/rsta.2008.016

    Article  Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758. doi:10.1175/2009JCLI3361.1

    Article  Google Scholar 

  • Knutti R, Sedláˇcek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Change 3:369–373. doi:10.1038/NCLIMATE1716

    Article  Google Scholar 

  • Kothawale DR, Revadekar JV, Rupa Kumar K (2010) Recent trends in pre-monsoon daily temperature extremes over India. J Earth Syst Sci 119:51–65

    Article  Google Scholar 

  • Kothawale DR, Rupa Kumar K (2005) On the recent changes in surface temperature trends over India. Geophys Res Lett 32:L18714. doi:10.1029/2005GL023528

    Article  Google Scholar 

  • Kousari MR, Ahani H, Zadeh RH (2013) Temporal and spatial trend detection of maximum air temperature in Iran during 1960–2005. Glob Planet Chang 111:97–110. doi:10.1016/j.gloplacha.2013.08.011

    Article  Google Scholar 

  • Kripalani RH, Oh JH, Kulkarni A, Sabade SS, Chaudhari HS (2007) South Asian summer monsoon precipitation variability: coupled climate model simulations and projections under IPCC AR4. Theor Appl Climatol 90:133–159

    Article  Google Scholar 

  • Kumari PB, Londhe AL, Daniel S, Jadhav DB (2007) Observational evidence of solar dimming: offsetting surface warming over India. Geophys Res Lett 34:L21810. doi:10.1029/2007GL031133

    Article  Google Scholar 

  • Kundzewicz ZW, Robson AJ (2004) Change detection in hydrological records—a review of the methodology. Hydrol Sci J 49:7–19

    Article  Google Scholar 

  • Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res 115:D10101. doi:10.1029/2009JD012882

    Article  Google Scholar 

  • Liu M, Tian H, Yang Q, Yang J, Song X, Lohrenz SE, Cai WJ (2013) Long-term trends in evapotranspiration and runoff over the drainage basins of the Gulf of Mexico during 1901–2008. Water Resour Res 49:1988–2012. doi:10.1002/wrcr.20180

    Article  Google Scholar 

  • Lucarini V, Calmanti S, Dell’Aquila A, Ruti PM, Speranza A (2007) Intercomparison of the northern hemisphere winter mid-latitude atmospheric variability of the IPCC models. Clim Dyn 28:829–848. doi:10.1007/s00382-006-0213-x

    Article  Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Martinez CJ, Maleski JJ, Miller MF (2012) Trends in precipitation and temperature in Florida, USA. J Hydrol 452:259–281. doi:10.1016/j.jhydrol.2012.05.066

    Article  Google Scholar 

  • Maurer EP, Stewart IT, Bonfils C, Duffy PB, Cayan D (2007) Detection, attribution, and sensitivity of trends toward earlier stream flow in the Sierra Nevada. J Geophys Res 112:D11118

    Article  Google Scholar 

  • Maxino CC, McAvaney BJ, Pitman AJ, Perkins SE (2008) Ranking the AR4 climate models over the Murray-Darling Basin using simulated maximum temperature, minimum temperature and precipitation. Int J Climatol 28:1097–1112. doi:10.1002/joc.1612

    Article  Google Scholar 

  • McFarlane D, Stone R, Martens S, Thomas J, Silberstein R, Ali R, Hodgson G (2012) Climate change impacts on water yields and demands in south-western Australia. J Hydrol 475:488–498. doi:10.1016/j.jhydrol.2012.05.038

    Article  Google Scholar 

  • Meehl GA, Covey C, Worth TD, Latif M, Mcavaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel dataset a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394

    Article  Google Scholar 

  • Mishra V, Lettenmaier DP (2011) Climatic trends in major U.S urban areas, 1950–2009. Geophys Res Lett 38:L16401. doi:10.1029/2011GL048255

    Article  Google Scholar 

  • Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grid points. Int J Climatol 25:693–712

    Article  Google Scholar 

  • Morak S, Hegerl GC, Christidis N (2013) Detectable changes in the frequency of temperature extremes. J Clim 26:1561–1574. doi:10.1175/JCLI-D-11-00678.1

    Article  Google Scholar 

  • Mueller B, Seneviratne SI (2012) Hot days induced by precipitation deficits at the global scale. Proc Natl Acad Sci 109:12398–12403. doi:10.1073/pnas.1204330109

    Article  Google Scholar 

  • Nicholls N, Della-Marta P, Collins D (2004) 20th century changes in temperature and rainfall in New South Wales. Aust Meteorol Mag 47:263–268

    Google Scholar 

  • Nieto S, Rodríguez-Puebla C (2006) Comparison of precipitation from observed data and general circulation models over the Iberian Peninsula. J Clim 19:4254–4275

    Article  Google Scholar 

  • Pant GB, Rupa Kumar K (1997) Climates of South Asia. John Wiley & Sons, Chichester, UK, p 310

  • Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond A 193:120–145

    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. doi:10.1175/JCLI4253.1

    Article  Google Scholar 

  • Perkins SE, Pitman AJ, Sisson SA (2013) Systematic differences in future 20 year temperature extremes in AR4 model projections over Australia as a function of model skill. Int J Climatol 33:1153–1167. doi:10.1002/joc.3500

    Article  Google Scholar 

  • Piani C, Haerter JO, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. doi:10.1007/s00704-009-0134-9

    Article  Google Scholar 

  • Piani C, Weedon G, Best M, Gomes S, Viterbo P, Hagemann S, Haerter J (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215. doi:10.1016/j.jhydrol.2010.10.024

    Article  Google Scholar 

  • Pitman AJ, Perkins SE (2007) Reducing uncertainty in selecting climate models for hydrological impact assessments, in Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resource Management, Symposium HS2004 at IUGG2007, Perugia, IAHS Publication 313, IAHS Press; 3–15

  • Pitman AJ, Perkins SE (2009) Global and regional comparison of daily 2-m and 1000-hPa maximum and minimum temperatures in three global reanalyses. J Clim 22:4667–4681. doi:10.1175/2009JCLI2799.1

    Article  Google Scholar 

  • Power S, Tseitkin F, Torok S, Lavery B, McAvaney B (1998) Australian temperature, Australian rainfall, and the Southern Oscillation, 1910–1996: coherent variability and recent changes. Aust Meteorol Mag 47:85–101

    Google Scholar 

  • Preethi B, Kripalani RH (2010) Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models. Clim Dyn 35:1521–1539. doi:10.1007/s00382-009-0657-x

    Article  Google Scholar 

  • Rajeevan M, Nanjundiah RS (2009) Coupled model simulations of twentieth century climate of the Indian summer monsoon. Current trends in science, Indian Academy of Sciences India, 537–567. (http://www.ias.ac.in/academy/pjubilee/book.html)

  • Raju KS, Nagesh Kumar D (2014) Ranking of global climatic models for India using multicriterion Analysis. Clim Res 60:103–117

    Article  Google Scholar 

  • Santer BD, Painter JF, Mears CA, Doutriaux C, Caldwell P et al (2013) Identifying human influences on atmospheric temperature. Proc Natl Acad Sci 110:26–33. doi:10.1073/pnas.1210514109

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sonali P, Nagesh Kumar D (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227. doi:10.1016/j.jhydrol.2012.10.034

    Article  Google Scholar 

  • Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 vs CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744. doi:10.1007/s00382-012-1607-6

    Article  Google Scholar 

  • Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 10:249–254

    Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  • Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes—an intercomparison of model simulated historical & future changes in extreme events. Clim Chang 79:185–211. doi:10.1007/s10584-006-9051-4

    Article  Google Scholar 

  • Vuuren VDP, Meinshausen M, Plattner GK, Joos F et al (2008) Temperature increase of 21st century mitigation scenarios. Proc Natl Acad Sci 105:15258–15262. doi:10.1073/pnas.0711129105

    Article  Google Scholar 

  • Wardle R, Smith I (2004) Modeled response of the Australian monsoon to changes in land surface temperatures. Geophys Res Lett 31:L16205. doi:10.1029/2004GL020157

    Article  Google Scholar 

  • Wei J, Dirmeyer PA, Guo Z, Zhang L, Misra V (2010) How much do different land models matter for climate simulation? Part I: climatology and variability. J Clim 23:3120–3134. doi:10.1175/2010JCLI3177.1

    Article  Google Scholar 

  • Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multi-model climate projections. J Climate 23:4175–4191. doi:10.1175/2010JCLI3594.1

    Article  Google Scholar 

  • Wen QH, Zhang X, Xu Y, Wang B (2013) Detecting human influence on extreme temperatures in China. Geophys Res Lett 40:1171–1176. doi:10.1002/grl.50285

    Article  Google Scholar 

  • Whetton P, Macadam I, Bathols J, Grady JO (2007) Assessment of the use of current climate patterns to evaluate regional enhanced greenhouse response patterns of climate models. Geophys Res Lett 34:14. doi:10.1029/2007GL030025

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridha V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

    Article  Google Scholar 

  • Xu YP, Zhang X, Ran Q, Tian Y (2013) Impact of climate change on hydrology of upper reaches of Qiantang River Basin, East China. J Hydrol 483:51–60. doi:10.1016/j.jhydrol.2013.01.004

    Article  Google Scholar 

  • Yao Y, Luo Y, Huang J, Zhao Z (2013) Comparison of monthly temperature extremes simulated by CMIP3 and CMIP5 models. J Climdoi 26:7692–7707. doi:10.1175/JCLI-D-12-00560.1

    Article  Google Scholar 

  • Yue S, Pilon P, Cavadias G (2002a) Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259:254–271

    Article  Google Scholar 

  • Yue S, Pilon P, Phinney B, Cavadias G (2002b) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16:1807–1829

    Article  Google Scholar 

  • Yue S, Wang CY (2004) The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Res Manage 18:201–218

    Article  Google Scholar 

  • Zhang D, Liu X, Hong H (2013) Assessing the effect of climate change on reference evapotranspiration in China. Stoch Environ Res Risk Assess 27:1871–1881. doi:10.1007/s00477-013-0723-0

    Article  Google Scholar 

  • Zhou T, Yu R (2006) Twentieth-century surface air temperature over china and the globe simulated by coupled climate models. J Clim 19:5843–5858

    Article  Google Scholar 

  • Zhou L, Dickinson RE, Dai A, Dirmeyer P (2010) Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: comparing multi-model simulations with observations. Clim Dyn 35:1289–1307. doi:10.1007/s00382-009-0644-2

    Article  Google Scholar 

Download references

Acknowledgment

This work is partly supported by the Ministry of Earth Sciences, Govt. of India (MOES/ATMOS/PP-IX/09). We acknowledge the assistance of the World Climate Research Program’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model outputs. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led to the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank IMD for gridded temperature dataset.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Nagesh Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sonali, P., Kumar, D.N. & Nanjundiah, R.S. 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 (2017). https://doi.org/10.1007/s00704-015-1716-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-015-1716-3

Keywords

Navigation