Advertisement

Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon

  • Popat Salunke
  • Shipra Jain
  • Saroj Kanta Mishra
Original Paper

Abstract

In this paper, the performance of 28 CMIP5 models in simulating the climate of the Himalaya-Tibetan Plateau (HTP) for the recent past (1975–2005) is evaluated using the observations from the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE). Many models realistically simulate the spatial distribution of surface air temperature (Tas) and precipitation with pattern correlation as high as 0.8; however, they possess severe biases in their magnitude. The biases in Tas appear to be associated with the biases in the surface elevation. All the models capture the observed phase of the annual cycle of the Tas but underestimate the amplitude. For precipitation, the phase is captured by most models (except few), but the amplitude is overestimated in all models. In the mid-intensity precipitation range (10–80 mm day−1), most of the models overestimate the probability of occurrence and show large intermodel differences. Most of the models fail to simulate the spatial distribution of the trend in Tas and precipitation. As compared to many individual models, the biases are noted to reduce when using multimodel means (MMMs); however, the MMMs also failed to capture the observed trends in both Tas and precipitation. Many models still struggle to capture the large-scale phenomena, such as the location and intensity of upper-level Asian anticyclone and middle troposphere temperature maximum over the HTP, which have large implications on the HTP as well as the Indian summer monsoon. The results show that none of the models capture all features of the HTP monsoon, and hence, further improvement in the parameterization schemes and resolution is required to gain more confidence in the projection of HTP climate using these models.

Notes

Acknowledgments

The authors thank the World Climate Research Programme and Earth System Grid Federation (ESGF) for providing CMIP5 historical data. We acknowledge NCAR for providing the NCL software used for plotting the data. The various modeling groups are sincerely thanked for producing and making available their model output. The TRMM and APHRODITE datasets are obtained from the National Aeronautics and Space Administration (NASA) and National Center for Atmospheric Research (NCAR). The CRU, ERA-Interim and GTOPO30 data are used in this study. PS is thankful to the Ministry of Human Resource Development and Indian Institute of Technology, Delhi for providing his Ph.D. fellowship. The authors also thank the two anonymous reviewers for the valuable comments and helpful suggestions, which have greatly improved the original manuscript.

Funding information

This work is supported by the DST Centre of Excellence in Climate Modeling, Indian Institute of Technology, Delhi, India.

References

  1. Arora VK, Scinocca JF, Boer GJ, Christian JR, Denman KL, Flato GM, Kharin VV, Lee WG, Merryfield WJ (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38:L05805.  https://doi.org/10.1029/2010GL046270 CrossRefGoogle Scholar
  2. Bi D et al (2012) ACCESS: the Australian coupled climate model for IPCC AR5 and CMIP5. In: Climate change Beijing. Chinese Academy of Science, Beijing, ChinaGoogle Scholar
  3. Boos WR, Kuang Z (2010) Dominant control of the South Asian monsoon by orographic insulation versus plateau heating. Nature 463(7278):218–222CrossRefGoogle Scholar
  4. Chapman WL, Walsh JE (2007) A synthesis of Antarctic temperatures. J Clim 20:4096–4117CrossRefGoogle Scholar
  5. Chen LX, Reiter ER, Feng ZQ (1985) The atmospheric hear-source over the Tibetan Plateau-May–August 1979. Mon Wea Rev 113:1771–1790CrossRefGoogle Scholar
  6. Christensen JH et al (2007) Regional climate projections. In: Solomon et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 847–940Google Scholar
  7. Collins M, Tett SFB, Cooper C (2001) The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 17:61–81CrossRefGoogle Scholar
  8. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  9. Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz JC, Ginoux P, Lin SJ, Schwarzkopf MD, Austin J, Alaka G, Cooke WF, Delworth TL, Freidenreich SM, Gordon CT, Griffies SM, Held IM, Hurlin WJ, Klein SA, Knutson TR, Langenhorst AR, Lee HC, Lin Y, Magi BI, Malyshev SL, Milly PCD, Naik V, Nath MJ, Pincus R, Ploshay JJ, Ramaswamy V, Seman CJ, Shevliakova E, Sirutis JJ, Stern WF, Stouffer RJ, Wilson RJ, Winton M, Wittenberg AT, Zeng F (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24:3484–3519CrossRefGoogle Scholar
  10. Duan A, Hu J, Xiao Z (2013) The Tibetan Plateau summer monsoon in the CMIP5 simulations. J Clim 26(19):7747–7766CrossRefGoogle Scholar
  11. Duan Q, Phillips TJ (2010) Bayesian estimation of local signal and noise in multimodel simulations of climate change. J Geophys Res 115:D18123CrossRefGoogle Scholar
  12. Duan A, Wu G (2005) Role of the Tibetan Plateau thermal forcing in the summer climate patterns over subtropical Asia. Clim Dyn 24:793–807CrossRefGoogle Scholar
  13. Duan AM, Wu GX, Liu YM, Mao YM, Zhao P (2012) Weather and climate effects of the Tibetan Plateau. Adv Atmos Sci 29:978–992 (in Chinese)CrossRefGoogle Scholar
  14. Duan AM, Wu GX, Zhang Q, Liu YM (2006) New proofs of the recent climate warming over the Tibetan Plateau as a result of the increasing greenhouse gases emissions. Chin Sci Bull 51(11):1396–1400CrossRefGoogle Scholar
  15. Dufresne JL, Foujols MA, 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 40:2123–2165CrossRefGoogle Scholar
  16. 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 24:4973–4991CrossRefGoogle Scholar
  17. Gesch DB, Verdin KL, Greenlee SK (1999) New land surface digital elevation model covers the earth. Eos, Trans Am Geophys Union 80(6):69–70CrossRefGoogle Scholar
  18. Hahn DG, Manabe S (1975) The role of mountains in the South Asian monsoon circulation. J Atmos Sci 32:1515–1541CrossRefGoogle Scholar
  19. Harris IC, Jones PD (2017) CRU TS4.00: Climatic Research Unit (CRU) Time-Series (TS) version 4.00 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901–Dec. 2015). Centre for Environmental Data Analysis (CEDA).  https://doi.org/10.5285/edf8febfdaad48abb2cbaf7d7e846a86
  20. Hazeleger W, Severijns C, Semmler T, Ştefănescu S, Yang S, Wang X, Wyser K, Dutra E, Baldasano JM, Bintanja R, Bougeault P, Caballero R, Ekman AML, Christensen JH, van den Hurk B, Jimenez P, Jones C, Kållberg P, Koenigk T, McGrath R, Miranda P, van Noije T, Palmer T, Parodi JA, Schmith T, Selten F, Storelvmo T, Sterl A, Tapamo H, Vancoppenolle M, Viterbo P, Willén U (2010) EC-earth: a seamless Earth system prediction approach in action. Bull Am Meteorol Soc 91:1357–1363CrossRefGoogle Scholar
  21. Hsu HH, Liu X (2003) Relationship between the Tibetan Plateau heating and East Asian summer monsoon rainfall. Geophys Res Lett 30(20):2066CrossRefGoogle Scholar
  22. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Stocker EF, Wolff B (2007) The TRMM multi-satellite precipitation analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J Hydrometeorol 8:33–55CrossRefGoogle Scholar
  23. Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo KO, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque JF, Law RM, Meinshausen M, Osprey S, Palin EJ, Parsons Chini L, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570.  https://doi.org/10.5194/gmd-4-543-2011 CrossRefGoogle Scholar
  24. Kang S, Zhang Y, Qin D, Ren J, Zhang Q, Grigholm B, Mayewski PA (2007) Recent temperature increase recorded in an ice core in the source region of Yangtze River. Chin Sci Bull 52(6):825–831CrossRefGoogle Scholar
  25. Kawazoe S, Gutowski W (2013) Regional, very heavy daily precipitation in CMIP5 simulations. J Hydrometeorol 14:1228–1242CrossRefGoogle Scholar
  26. Kim D, Sobel AH, del Genio AD, Chen Y, Camargo SJ, Yao MS, Kelley M, Nazarenko L (2012) The tropical sub-seasonal variability simulated in the NASA GISS general circulation model. J Clim 25:4641–4659CrossRefGoogle Scholar
  27. Li CF, Yanai M (1996) The onset and interannual variability of the Asian summer monsoon in relation to land–sea thermal contrast. J Climate 9:358–375CrossRefGoogle Scholar
  28. Li Q, Jiang JH, Wu DL, Read WG, Livesey NJ, Waters JW, Zhang Y, Wang B, Filipiak MJ, Davis CP, Turquety S, Wu S, Park RJ, Yantosca RM, Jacob DJ (2005) Convective outflow of South Asian pollution: a global CTM simulation compared with EOS MLS observations. Geophys Res Lett 32:L14826.  https://doi.org/10.1029/2005GL022762 CrossRefGoogle Scholar
  29. Li LJ et al (2013) The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2. Adv Atmos Sci 30:543CrossRefGoogle Scholar
  30. Liu X, Chen B (2000) Climatic warming in the Tibetan Plateau during recent decades. Int J Climatol 20:1729–1742CrossRefGoogle Scholar
  31. Mao J, Robock A (1998) Surface air temperature simulations by AMIP general circulation models: volcanic and ENSO signals and systematic errors. J Clim 11:1538–1552CrossRefGoogle Scholar
  32. Merryfield WJ, Lee WS, Boer GJ, Kharin VV, Scinocca JF, Flato GM, Polavarapu S (2012) The Canadian seasonal to interannual prediction system. Part I: models and initialization. Mon Weather Rev 141:2910–2945CrossRefGoogle Scholar
  33. Phillips TJ, Gleckler PJ (2006) Evaluation of continental precipitation in 20th century climate simulations: the utility of multimodel statistics. Water Resour Res 42:W03202CrossRefGoogle Scholar
  34. Qiu J (2008) China: the third pole. Nature 454:393–396CrossRefGoogle Scholar
  35. Rajagopalan B, Molnar P (2013) Signatures of Tibetan Plateau heating on Indian summer monsoon rainfall variability. J Geophys Res Atmos 118:1170–1178CrossRefGoogle Scholar
  36. Rotstayn LD et al (2010) Improved simulation of Australian climate and ENSO-related rainfall variability in a GCM with an interactive aerosol treatment. Int J Climatol 30:1067–1088Google Scholar
  37. Sakamoto TT et al (2012) MIROC4h—a new high resolution atmosphere-ocean coupled general circulation model. J Meteor Soc Japan 90:325–359CrossRefGoogle Scholar
  38. Sato T, Kimura F (2007) How does the Tibetan Plateau affect the transition of Indian monsoon rainfall? Mon Weather Rev 135(5):2006–2015CrossRefGoogle Scholar
  39. Song JH, Kang HS, Byun YH, Hong SY (2010) Effects of the Tibetan Plateau on the Asian summer monsoon: a numerical case study using a regional climate model. Int J Climatol 30:743–759Google Scholar
  40. Su F, Duan X, Chen D, Hao Z, Cuo L (2013) Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J Clim 26:3187–3208.  https://doi.org/10.1175/JCLI-D-12-00321.1 CrossRefGoogle Scholar
  41. Tang MC, Reiter ER (1984) Plateau monsoon of the northern hemisphere: a comparison between North America and Tibet. Mon Weather Rev 112:617–637CrossRefGoogle Scholar
  42. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192CrossRefGoogle Scholar
  43. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498CrossRefGoogle Scholar
  44. Thompson L, Yao T, Mosley-Thompson E, Davis M, Henderson K, Lin PN (2000) A high-resolution millennial record of the South Asian monsoon from Himalayan ice cores. Science 289:1916–1919CrossRefGoogle Scholar
  45. Tian L, Yao T, MacClune K, White JWC, Schilla A, Vaughn B, Ichiyanagi K (2007) Stable isotopic variations in West China: a consideration of moisture sources. J Geophys Res 112:D10112CrossRefGoogle Scholar
  46. Voldoire A et al (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Climate Dyn 40:2091–2121.  https://doi.org/10.1007/s00382-011-1259-y CrossRefGoogle Scholar
  47. Volodin EM, Diansky NA, Gusev AV (2010) Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv Atmos Oceanic Phys 46:414–431CrossRefGoogle Scholar
  48. 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-CHEM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4:845–872CrossRefGoogle Scholar
  49. 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 23:6312–6335CrossRefGoogle Scholar
  50. Webster PJ, Magana VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and prospects for prediction. J Geophys Res 103(C7):14451–14510CrossRefGoogle Scholar
  51. Wu GX (1984) The nonlinear response of the atmosphere to large-scale mechanical and thermal forcing. J Atmos Sci 41:2456–2476CrossRefGoogle Scholar
  52. Wu GX, Zhang YS (1998) Tibetan Plateau forcing and the timing of the monsoon onset over South Asia and the South China Sea. Mon Weather Rev 126:913–27CrossRefGoogle Scholar
  53. Wu G, Duan A, Liu Y, Mao J, Ren R, Bao Q, He B, Liu HW (2015) Tibetan Plateau climate dynamics: recent research progress and outlook. Natl Sci Rev 2(1):100–116CrossRefGoogle Scholar
  54. Xin X, Wu T, Zhang J (2013) Introduction of CMIP5 simulations carried out with the climate system models of Beijing Climate Center. Adv Clim Chang Res 4:41–49 (in Chinese)CrossRefGoogle Scholar
  55. Yanai M, Li C, Song Z (1992) Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon. J Meteor Soc Japan 70:319–351CrossRefGoogle Scholar
  56. Yasutomi N, Hamada A, Yatagai A (2011) Development of a long-term daily gridded temperature dataset and its application to rain/snow discrimination of daily precipitation. Global Environ Res V15N2:165–172Google Scholar
  57. Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93:1401–1415.  https://doi.org/10.1175/BAMS-D-11-00122.1 CrossRefGoogle Scholar
  58. Ye DZ, Gao YX (1979) Climatology of Qinghai-Xizang (Tibetan) Plateau. Science Press, Beijing, p 279Google Scholar
  59. Ye DZ, Wu GX (1998) The role of the heat source of the Tibetan Plateau in the general circulation. Meteorog Atmos Phys 67:181–198CrossRefGoogle Scholar
  60. Yeh TC, Gao YX (1979) The meteorology of the Qinghai-Tibetan Plateau. Science Press, Beijing, p 278Google Scholar
  61. Yukimoto S et al (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3-model description and basic performance. J Meteor Soc Japan 90A:23–64CrossRefGoogle Scholar
  62. Zanchettin D, Rubino A, Matei D, Bothe O, Jungclaus JH (2013) Multidecadal-to-centennial SST variability in the MPIESM simulation ensemble for the last millennium. Clim Dyn 40(5–6):1301–1318CrossRefGoogle Scholar
  63. Zhang ZS, Nisancioglu K, Bentsen M, Tjiputra J, Bethke I, Yan Q, Risebrobakken B, Andersson C, Jansen E (2012) Pre-industrial and mid-Pliocene simulations with NorESM-L. Geosci Model Dev 5:523–533CrossRefGoogle Scholar
  64. Zhao H, Moore GWK (2004) On the relationship between Tibetan snow cover, the Tibetan plateau monsoon and the Indian summer monsoon. Geophys Res Lett 31:L14204CrossRefGoogle Scholar
  65. Zheng D, Zhang Q, Wu S (eds) (2000) Mountain geoecology and sustainable development of the Tibetan Plateau. Geo Journal Library Series Vol. 57 Springer 394Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Popat Salunke
    • 1
  • Shipra Jain
    • 1
  • Saroj Kanta Mishra
    • 1
  1. 1.Centre for Atmospheric Sciences (CAS)Indian Institute of Technology, Delhi (IITD)DelhiIndia

Personalised recommendations