Climate Dynamics

, Volume 47, Issue 9–10, pp 3253–3270 | Cite as

Evaluating CMIP5 models using GPS radio occultation COSMIC temperature in UTLS region during 2006–2013: twenty-first century projection and trends

  • P. Kishore
  • Ghouse Basha
  • M. Venkat Ratnam
  • Isabella Velicogna
  • T. B. M. J. Ouarda
  • D. Narayana Rao
Article
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Accepted:

Abstract

This paper provides a first overview of the performance of global climate models participating in the Coupled Model Inter-Comparison Project phase 5 (CMIP5) in simulating the upper troposphere and lower stratosphere (UTLS) temperatures. Temperature from CMIP5 models is evaluated with high resolution global positioning system radio occultation (GPSRO) constellation observing system for meteorology, ionosphere, and climate (COSMIC) data during the period of July 2006–December 2013. Future projections of 17 CMIP5 models based on the representative concentration pathway (RCP) 8.5 scenarios are utilized to assess model performance and to identify the biases in the temperature in the UTLS region at eight different pressure levels. The evaluations were carried out vertically, regionally, and globally to understand the temperature uncertainties in CMIP5 models. It is found that the CMIP5 models successfully reproduce the general features of temperature structure in terms of vertical, annual, and inter-annual variation. The ensemble mean of CMIP5 models compares well with the COSMIC GPSRO data with a mean difference of ±1 K. In the tropical region, temperature biases vary from one model to another. The spatial difference between COSMIC and ensemble mean reveals that at 100 hPa, the models show a bias of about ±2 K. With increase in altitude the bias decreases and turns into a cold bias over the tropical and Antarctic regions. The future projections of the CMIP5 models were presented during 2006–2099 under the RCP 8.5 scenarios. Projections show a warming trend at 300, 200, and 100 hPa levels over a wide region of 60°N–45°S. The warming decreases rapidly and becomes cooling with increase in altitudes by the end of twenty-first century. Significant cooling is observed at 30, 20, and 10 hPa levels. At 300/10 hPa, the temperature trend increases/decreases by ~0.82/0.88 K/decade at the end of twenty-first century under RCP 8.5 scenarios.

Keywords

UTLS Evaluation Temperature CMIP5 COSMIC GPSRO Projections Trends 
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Notes

Acknowledgments

We acknowledge the GCM modeling groups, the Program for Climate Model Diagnosis and Inter-comparison (PCMDI), and the WCRP’s Working Group on Coupled Modeling for their roles in making available the WCRP CMIP5 multi-model datasets. The authors would like to thank all the members of COSMIC Data Analysis and Archival Center (CDAC) team for providing the COSMIC data used in this study. The authors wish to thank the Editor, Prof. Jianping Li and two anonymous reviewers whose comments contributed to the improvement of the quality of the paper.

References

  1. Anthes RA, Bernhardt PA, Chen YZ (2008) The COSMIC/FORMOSAT-3 mission: early results. Bull Am Meteorol Soc 89:313–333. doi: 10.1175/BAMS-89-3-313 CrossRefGoogle Scholar
  2. Ao CO, Jiang JH, Mannucci AJ et al (2015) Evaluation of CMIP5 upper troposphere and lower stratosphere geopotential height with GPS radio occultation observations. J Geophys Res 120:1678–1689. doi: 10.1002/2014JD022239 Google Scholar
  3. 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. doi: 10.1029/2010gl046270 CrossRefGoogle Scholar
  4. Charlton-Perez AJ, Baldwin MP, Birner T et al (2013) On the lack of stratospheric dynamical variability in low-top versions of the CMIP5 models. J Geophys Res Atmos 118:2494–2505. doi: 10.1002/jgrd.50125 CrossRefGoogle Scholar
  5. Foelsche U, Kirchengast G, Steiner AK, Kornblueh L, Manzini E, Bengtsson L (2008) An observing system simulation experiment for climate monitoring with GNSS radio occultation data: setup and test bed study. J Geophys Res Atmos 113:D11108. doi: 10.1029/2007JD009231 CrossRefGoogle Scholar
  6. Garny H, Dameris M, Randel, WJ, Bodeker, GE, Deckert R (2011) Dynamically forced increase of tropical upwelling in the lower stratosphere. J Atmos Sci 68:1214CrossRefGoogle Scholar
  7. Gates WL, Boyle JS, Covey C et al (1999) An overview of the results of the atmospheric model intercomparison project (AMIP I). Bull Am Meteorol Soc 80:29–55CrossRefGoogle Scholar
  8. 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. doi: 10.1175/2011JCLI4083.1 CrossRefGoogle Scholar
  9. Griffies SM, Winton M, Donner LJ et al (2011) The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J Clim 24:3520–3544CrossRefGoogle Scholar
  10. Hajj GA, de la Torre Juarez M, Iijima BA, Kursinski ER, Mannucci AJ, Yunck TP (2002) GPS radio occultations coming of age: spacecraft launches add two new instruments for climate monitoring. EoS Trans AGU 83:37–38CrossRefGoogle Scholar
  11. He W, Ho S-P, Chen H, Zhou X, Hunt D, Kuo YH (2009) Assessment of radiosonde temperature measurements in the upper troposphere and lower stratosphere using COSMIC radio occultation data. Geophys Res Lett 36:L17807. doi: 10.1029/2009GL038712 CrossRefGoogle Scholar
  12. Holland PW, Welsch RE (1977) Robust regression using iteratively reweighted least-squares. Commun Stat A6:813–827CrossRefGoogle Scholar
  13. Holton JR, Haynes PH, McIntyre ME, Douglass AR, Rood RB, Pfister L (1995) Stratosphere–troposphere exchange. Rev Geophy 33:403–439CrossRefGoogle Scholar
  14. John VO, Soden BJ (2007) Temperature and humidity biases in global climate models and their impact on climate feedbacks. Geophys Res Lett 34:L18704. doi: 10.1029/2007gl030429 CrossRefGoogle Scholar
  15. Jones CD, Hughes JK, Bellouin N et al (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570. doi: 10.5194/gmd-4-543-2011 CrossRefGoogle Scholar
  16. Kim JK, Grise M, Son S-W (2013) Thermal characteristics of the cold-point tropopause region in CMIP5 models. J Geophys Res Atmos 118:8827–8841. doi: 10.1002/jgrd.50649 CrossRefGoogle Scholar
  17. Kishore P, Namboothiri SP, Jiang JH, Sivakumar V, Igarashi K (2009) Global temperature estimates in the troposphere and stratosphere: a validation study of COSMIC/FORMOSAT-3 measurements. Atmos Chem Phys 9:897–908. doi: 10.5194/acp-9-897-2009 CrossRefGoogle Scholar
  18. Kursinski E, Hajj GA, Schofield JT, Linfield RP, Hardy KR (1997) Observing earth’s atmosphere with radio occultation measurements using the global positioning system. J Geophys Res Atmos 102:D23429–D23465CrossRefGoogle Scholar
  19. Kursinski E, Healy S, Romans L (2000) Initial results of combining GPS occultations with ECMWF global analyses within a 1DVAR framework. Earth Planets Space 52:885–892CrossRefGoogle Scholar
  20. Kutner MH, Nachtsheim CJ, Neter J, Li W (2011) Applied linear statistical models, 5th edn. McGraw-Hill/Irwin, BostonGoogle Scholar
  21. Lackner BC, Steiner AK, Hegerl GC, Kirchengast G (2011) Atmospheric climate change detection by radio occultation data using a fingerprinting method. J Clim 24:5275–5291CrossRefGoogle Scholar
  22. Lu J, Vecchi GA, Reichler T (2007) Expansion of the Hadley cell under global warming. Geophys Res Lett. doi: 10.1029/2006GL028443 Google Scholar
  23. McNally AP (2002) A note on the occurrence of cloud in meteorologically sensitive areas and the implications for advanced infrared sounders. Q J R Meteorol Soc 128:2551–2556. doi: 10.1256/qj.01.206 CrossRefGoogle Scholar
  24. Mears CA, Wentz FJ, Thorne P, Bernie D (2011) Assessing uncertainty in estimates of atmospheric temperature changes From MSU and AMSU using a Monte-Carlo estimation technique. J Geophys Res 116:D08112. doi: 10.1029/2010JD014954 CrossRefGoogle Scholar
  25. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  26. Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler KG, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Clim Dyn 29(6):565–574. doi: 10.1007/s00382-007-0247-8.dcccc CrossRefGoogle Scholar
  27. Randel WJ, Garcia RR, Wu F (2008) Dynamical balances and tropical stratospheric upwelling. J Atmos Sci 65:3584–3595CrossRefGoogle Scholar
  28. Rao DN, Ratnam MV, Murthy BVK, Rao VVMJ, Mehta SK, Nath D, Basha G (2007) Identification of tropopause using bending angle profile from GPS radio occultation (RO): a radio tropopause. Geophys Res Lett 34:L15809. doi: 10.1029/2007GL029709 CrossRefGoogle Scholar
  29. Rao DN, Ratnam MV, Mehta S, Nath D, Basha G, Rao VVMJ, Murthy BVK, Tsuda T, Nakamura K (2009) Validation of the COSMIC radio occultation data over Gadanki: a tropical region. Terr Atmos Ocean Sci 20:50–70CrossRefGoogle Scholar
  30. Ratnam MV, Sunilkumar SV, Parameswaran K et al (2014) Tropical tropopause dynamics (TTD) campaigns over Indian region: an overview. J Atmos Sol Terr Phys. doi: 10.1016/j.jastp.2014.05.007 Google Scholar
  31. Riahi K, Gruebler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Change 74(7):887–935CrossRefGoogle Scholar
  32. Rosenlof KH (1995) Seasonal cycle of the residual mean meridional circulation in the stratosphere. J Geophys Res 100:5173–5191CrossRefGoogle Scholar
  33. Rotstayn LD, Collier MA, Dix MR, Feng Y, Gordon HB, O’Farrell SP, Smith IN, Syktus J (2010) Improved simulation of Australian climate and ENSO-related rainfall variability in a global climate model with an interactive aerosol treatment. Int J Climatol 30(7):1067–1088. doi: 10.1002/joc.1952 Google Scholar
  34. Sakamoto TT, Komuro Y, Nishimura T (2012) MIROC4h–a new high-resolution atmosphere-ocean coupled general circulation model. J Meteorol Soc Jpn 90:325–359. doi: 10.2151/jmsj.2012-301 CrossRefGoogle Scholar
  35. Sausen R, Santer BD (2003) Use of changes in tropopause height to detect influences on climate. Meteorol Z 12(3):131–136CrossRefGoogle Scholar
  36. Schmidt GA, Ruedy R, Hansen JE (2006) Present day atmospheric simulations using giss modele: comparison to in situ satellite and reanalysis data. J Clim 19:153–192CrossRefGoogle Scholar
  37. Schreiner W, Rocken C, Sokolovskiy S, Syndergaard S, Hunt D (2007) Estimates of the precision of GPS radio occultations from the COSMIC/FORMOSAT-3 mission. Geophys Res Lett 34:L04808. doi: 10.1029/2006GL027557 CrossRefGoogle Scholar
  38. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor N, Miller HL (eds.) (2007) Climate Change 2007 – the Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change chap. Technical Summary Cambridge University Press Cambridge UK and New York NY USAGoogle Scholar
  39. Solomon S, Rosenlof KH, Portmann RW, Daniel JS, Davis SM, Sanford TJ, Plattner G-K (2010) Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 327:1219–1223. doi: 10.1126/science.1182488 CrossRefGoogle Scholar
  40. Steinbrecht W, Claude H, Kohler U, Hoinka KP (1998) Correction between tropopause height and total ozone: implication for long-term changes. J Geophys Res Atmos 103:19183–19192CrossRefGoogle Scholar
  41. Steiner AK, Hunt D, Ho S-P, Kirchengast G, Mannucci AJ, Scherllin-Pirscher B, Gleisner H, von Engeln A, Schmidt T, Ao C, Leroy SS, Kursinski ER, Foelsche U, Gorbunov M, Heise S, Kuo Y-H, Lauritsen KB, Marquardt C, Rocken C, Schreiner W, Sokolovskiy S, Syndergaard S, Wickert J (2013) Quantification of structural uncertainty in climate data records from GPS radio occultation. Atmos Chem Phys 13:1469–1484. doi: 10.5194/acp-13-1469-2013 CrossRefGoogle Scholar
  42. 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 CrossRefGoogle Scholar
  43. Thompson DW, Solomon S (2005) Recent stratospheric climate trends as evidenced in radiosonde data: global structure and tropospheric linkages. J Clim 18:4785–4795CrossRefGoogle Scholar
  44. Tian B, Fetzer EJ, Kahn BH, Teixeira J, Manning E, Hearty T (2013) Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. J Geophys Res Atmos 118:114–134. doi: 10.1029/2012JD018607 CrossRefGoogle Scholar
  45. Voldoire A, Sanchez-Gomez E, y Melia DS (2012) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn. doi: 10.1007/s00382-011-1259-y Google Scholar
  46. Volodin E, Dianskii N, Gusev A (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. doi: 10.1134/s000143381004002x CrossRefGoogle Scholar
  47. Wang L, Chen W (2013) A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int J Climatol. doi: 10.1002/joc.3822 Google Scholar
  48. Watanabe S, Hajima Sudo K et al (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3 m experiments. Geosci Model Dev 4(4):845–872. doi: 10.5194/gmd-4-845-2011 CrossRefGoogle Scholar
  49. Wu T, Song L, Li W, et al (2014) An overview of progress in climate system model development at the Beijing Climate Center applications for climate change studies. Acta Meteor Sinica 28:34–56, doi: 10.1007/s13351-014-3041-7
  50. Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka T, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3—model description and basic performance. J Meteorol Soc Jpn 90A:23–64. doi: 10.2151/jmsj.2012-A02 CrossRefGoogle Scholar
  51. 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(2):523–533. doi: 10.5194/gmd-5-523-2012 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • P. Kishore
    • 1
  • Ghouse Basha
    • 2
  • M. Venkat Ratnam
    • 3
  • Isabella Velicogna
    • 1
  • T. B. M. J. Ouarda
    • 2
    • 4
  • D. Narayana Rao
    • 5
  1. 1.Department of Earth System ScienceUniversity of CaliforniaIrvineUSA
  2. 2.Institute Center for Water and Environment (iWATER)Masdar Institute of Science and TechnologyAbu DhabiUAE
  3. 3.National Atmospheric Research LaboratoryGadankiIndia
  4. 4.INRS-ETE, National Institute of Scientific ResearchQuebec CityCanada
  5. 5.SRM Research InstituteSRM UniversityChennaiIndia

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