The contrasting response of Hadley circulation to different meridional structure of sea surface temperature in CMIP5

  • Juan Feng
  • Jianping Li
  • Jianlei Zhu
  • Yang Li
  • Fei Li
Original Paper
  • 59 Downloads

Abstract

The response of the Hadley circulation (HC) to the sea surface temperature (SST) is determined by the meridional structure of SST and varies according to the changing nature of this meridional structure. The capability of the models from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) is utilized to represent the contrast response of the HC to different meridional SST structures. To evaluate the responses, the variations of HC and SST were linearly decomposed into two components: the equatorially asymmetric (HEA for HC, and SEA for SST) and equatorially symmetric (HES for HC, and SES for SST) components. The result shows that the climatological features of HC and tropical SST (including the spatial structures and amplitude) are reasonably simulated in all the models. However, the response contrast of HC to different SST meridional structures shows uncertainties among models. This may be due to the fact that the long-term temporal variabilities of HEA, HES, and SEA are limited reproduced in the models, although the spatial structures of their long-term variabilities are relatively reasonably simulated. These results indicate that the performance of the CMIP5 models to simulate long-term temporal variability of different meridional SST structures and related HC variations plays a fundamental role in the successful reproduction of the response of HC to different meridional SST structures.

Notes

Acknowledgements

This work was supported by the SOA Program on Global Change and Air–Sea interactions (GASI-IPOVAI-03) and National Natural Science Foundation of China (41475076). The HadISST dataset was obtained from the Met Office Hadley Centre and is available online at http://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The NCEP/NCAR reanalysis and ERSST were obtained from NOAA and are available at http://www.esrl.noaa.gov/psd/data/gridded/. We acknowledge the WCRP’s Working Group on Coupled Modeling, which is responsible for CMIP5, and the climate modeling groups listed in Table 1 for their contribution to make the WCRP model output available.

Supplementary material

704_2018_2393_MOESM1_ESM.jpg (1.9 mb)
ESM 1 (JPEG 1.87 MB)
704_2018_2393_MOESM2_ESM.jpg (2.3 mb)
ESM 2 (JPEG 2.33 MB)
704_2018_2393_MOESM3_ESM.jpg (1.7 mb)
ESM 3 (JPEG 1.68 MB)
704_2018_2393_MOESM4_ESM.jpg (1.8 mb)
ESM 4 (JPEG 1.79 MB)
704_2018_2393_MOESM5_ESM.docx (14 kb)
ESM 5 (DOCX 14.1 KB)

References

  1. Bellenger H, Guiyardi E, Leloup J, Lengaigne M, Vialard J (2014) ENSO representation in climate models: from CMIP3 to CMIP5. Clim Dyn 42(7-8):1999–2018.  https://doi.org/10.1007/s00382-013-1783-z CrossRefGoogle Scholar
  2. Bentsen M, Bethke I, Debernard JB et al (2013) The Norwegian earth system model, NorESM1-M-part 1: description and basic evaluation of the physical climate. Geosci Model Dev 6(3):687–720.  https://doi.org/10.5194/gmd-6-687-2013 CrossRefGoogle Scholar
  3. Bhaskar J, Hu ZZ, Kumar A (2014) SST and ENSO variability and change simulated in historical experiments of CMIP5 models. Clim Dyn 42(7):2113–2124.  https://doi.org/10.1007/s00382-013-1803-z Google Scholar
  4. Chen G, Plumb RA, Lu J (2010) Sensitivities of zonal mean atmospheric circulation to SST warming in an aqua-planet model. Geophys Res Lett 37(12):L12701.  https://doi.org/10.1029/2010GL043473 CrossRefGoogle Scholar
  5. Chiang JC, Chang CY, Wehner MF (2013) Long-term behavior of the Atlantic interhemispheric SST gradient in the CMIP5 historical simulations. J Clim 26(21):8628–8640.  https://doi.org/10.1175/JCLI-D-12-00487.1 CrossRefGoogle Scholar
  6. Chylek P, Li J, Dubey MK, Wang M, Lesins G (2011) Observed and model simulated 20th century Arctic temperature variability: Canadian earth system model CanESM2. Atmos Chem Phys Discuss 11(8):22893–22907.  https://doi.org/10.5194/acpd-11-22893-2011 CrossRefGoogle Scholar
  7. Clement AC (2006) The role of the ocean in the seasonal cycle of the Hadley circulation. J Atmos Sci 63(12):3351–3365.  https://doi.org/10.1175/JAS3811.1 CrossRefGoogle Scholar
  8. Collier MA, Jeffrey SJ, Rotstayn LD, et al. (2011) The CSIRO-Mk3.6.0 Atmosphere–Ocean GCM: participation in CMIP5 and data publication. 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011. http://mssanz.org.au/modsim2011
  9. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Brönnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY, Jones PD, Kruk MC, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli Ø, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137(654):1–28.  https://doi.org/10.1002/qj.776 CrossRefGoogle Scholar
  10. 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(9):2123–2165.  https://doi.org/10.1007/s00382-012-1636-1 CrossRefGoogle Scholar
  11. Dunne JP, John JG, Shevliakova E, Stouffer RJ, Krasting JP, Malyshev SL, Milly PCD, Sentman LT, Adcroft AJ, Cooke W, Dunne KA, Griffies SM, Hallberg RW, Harrison MJ, Levy H, Wittenberg AT, Phillips PJ, Zadeh N (2013) GFDL’s ESM2 global coupled climate–carbon earth system models. Part II: carbon system formulation and baseline simulation characteristics. J Clim 26(7):2247–2267.  https://doi.org/10.1175/JCLI-D-12-00150.1 CrossRefGoogle Scholar
  12. Feng J, Li JP (2013) Contrasting impacts of two types of ENSO on the boreal spring Hadley circulation. J Clim 26(13):4773–4789.  https://doi.org/10.1175/JCLI-D-12-00298.1 CrossRefGoogle Scholar
  13. Feng R, Li JP, Wang JC (2011) Regime change of the boreal summer Hadley circulation and its connection with the tropical SST. J Clim 24(15):3867–3877.  https://doi.org/10.1175/2011JCLI3959.1 CrossRefGoogle Scholar
  14. Feng J, Li JP, Xie F (2013) Long-term variation of the principal mode of boreal spring Hadley circulation linked to SST over the Indo-Pacific warm pool. J Clim 26(2):532–544.  https://doi.org/10.1175/JCLI-D-12-00066.1 CrossRefGoogle Scholar
  15. Feng J, Li JP, Zhu JL et al (2015) Simulation of the equatorially asymmetric mode of the Hadley circulation in CMIP5 models. Adv Atmos Sci 32(8):1129–1142.  https://doi.org/10.1007/s00376-015-4157-0 CrossRefGoogle Scholar
  16. Feng J, Li JP, Jin FF et al (2016) Contrasting responses of the Hadley circulation to equatorially asymmetric and symmetric meridional sea surface temperature structures. J Clim 29(24):8949–8963.  https://doi.org/10.1175/JCLI-D-16-0171.1 CrossRefGoogle Scholar
  17. Feng J, Li JP, Jin FF et al (2017) The responses of the Hadley circulation to different meridional SST structures in the seasonal cycle. J Geophys Res Atmos 122(15):7785–7789.  https://doi.org/10.1002/2017JD026953 CrossRefGoogle Scholar
  18. Gastineau G, Treut HL, Li L (2008) Hadley circulation changes under global warming conditions indicated by coupled climate modes. Tellus 60A(5):863–884.  https://doi.org/10.1111/j.1600-0870.2008.00344.x CrossRefGoogle Scholar
  19. Gent PR, Danabasoglu G, Donner LJ et al (2011) The community climate system model version 4. J Clim 24(19):4973–4991.  https://doi.org/10.1175/2011JCLI4083.1 CrossRefGoogle Scholar
  20. Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg HD, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model Intercomparison project phase 5. J Adv Model Earth Syst 5(3):572–597.  https://doi.org/10.1002/jame.20038 CrossRefGoogle Scholar
  21. Gordon C, Cooper C, Senior CA et al (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16(2-3):147–168.  https://doi.org/10.1007/s003820050010 CrossRefGoogle Scholar
  22. Guilyard E, Bellenger H, Collins M et al (2012) A first look at ENSO in CMIP5. CLIVAR Exchange 17(1):29–32Google Scholar
  23. Guo YP, Li JP, Feng J et al (2016) The multidecadal variability of the asymmetric mode of the boreal autumn Hadley circulation and its link to the Atlantic multidecadal oscillation. J Clim 29(15):5625–5641.  https://doi.org/10.1175/JCLI-D-15-0025.1 CrossRefGoogle Scholar
  24. Han Z, Luo FF, Li SL et al (2016) Simulation by CMIP5 models of the Atlantic multidecadal oscillation and its climate impacts. Adv Atmos Sci 33(12):1329–1342.  https://doi.org/10.1007/s00376-016-5270-4 CrossRefGoogle Scholar
  25. Hazeleger W, Wang X, Severijns C et al (2011) EC-ERATH V2.2: description and validation of a new seamless earth system prediction model. Clim Dyn 39(11):1–19.  https://doi.org/10.1007/s00382-011-1228-5 Google Scholar
  26. Hou AY, Lindzen RS (1992) The influence of concentrated heating on the Hadley circulation. J Atmos Sci 49(14):1233–1241.  https://doi.org/10.1175/1520-0469(1992)049 CrossRefGoogle Scholar
  27. Hu YY, Tao LJ, Liu JP (2013) Poleward expansion of the Hadley circulation in CMIP5 simulations. Adv Atmos Sci 30(3):790–795.  https://doi.org/10.1007/s00376-01202187-4 CrossRefGoogle Scholar
  28. Ji DY, Wang LN, Feng J et al (2014) Description and basic evaluation of BNU-ESM version 1. Geosci Model Dev Discuss 7(5):1601–1647.  https://doi.org/10.5194/gmd-7-2039-2014 CrossRefGoogle Scholar
  29. Jiang Y, Luo Y, Zhao ZC (2010) Projection of wind speed changes in China in the 21st century by climate models. Chin J Atmos Sci 34:323–336Google Scholar
  30. Kalnay E, Kanamitsu M, Kilster R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–472.  https://doi.org/10.1175/1520-0477(1996)077 CrossRefGoogle Scholar
  31. Kug JS, Ham YG, Lee JY, Jin FF (2012) Improved simulation of two types of El Niño in CMIP5 models. Environ Res Lett 7(3):034002.  https://doi.org/10.1088/1748-9326/7/3/034002 CrossRefGoogle Scholar
  32. Li JP, Feng J (2017) Tropical large-scale atmospheric interaction in association with subtropical aridity trend. Fu CB, Mao HT (eds) Aridity trend in Northern China. World Scientific Publishing Co Pte Ltd, Singapore, pp 320Google Scholar
  33. Lindzen RS, Nigam S (1987) On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J Atmos Sci 44(17):2418–2436.  https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2 CrossRefGoogle Scholar
  34. Liu T, Li JP, Feng J et al (2016) Cross-seasonal relationship between the boreal autumn SAM and winter precipitation in the Northern Hemisphere in CMIP5. J Clim 29(18):6617–6636.  https://doi.org/10.1175/JCLI-D-15-0708.1 CrossRefGoogle Scholar
  35. Ma J, Li JP (2007) The reason for the strengthening of the boreal winter Hadley circulation and its connection with ENSO. Prog Nat Sci 17(11):1327–1333Google Scholar
  36. Ma J, Li JP (2008) The principal modes of variability of the boreal winter Hadley cell. Geophys Res Lett 35(1):L01808.  https://doi.org/10.1029/2007GL031883 CrossRefGoogle Scholar
  37. Martin GM, Bellouin N, Collins WJ et al (2011) The HadGEM2 family of Met Office unified model climate configurations. Geosci Model Dev 4(3):723–757.  https://doi.org/10.5194/gmd-4-723-2011 CrossRefGoogle Scholar
  38. Mitas CM, Clement A (2005) Has the Hadley cell been strengthening in recent 329 decades? Goephys Res Lett 32(3):L03809.  https://doi.org/10.1029/2004GL021765 Google Scholar
  39. Mitas CM, Clement A (2006) Recent behavior of the Hadley cell and tropical thermodynamics in climate models and reanalyses. Geophys Res Lett 33(1):L01810.  https://doi.org/10.1029/2005GL024406 CrossRefGoogle Scholar
  40. Neale RB, Chen CC, Gettelman A, et al. (2012) Description of the NCAR Community Atmospheric Model (CAM5.0). NCAR TECHNICAL NOTE, NCAR/TN-486+STR, http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf
  41. Oort AH, Yienger JJ (1996) Observed interannual variability in the Hadley circulation and its connection to ENSO. J Clim 9(11):2751–2767.  https://doi.org/10.1175/1520-0442(1996)009 CrossRefGoogle Scholar
  42. Qiao FL, Song ZY, Bao Y et al (2013) Development and evaluation of an earth system model with surface gravity waves. J Geophys Res 118(9):4514–4524.  https://doi.org/10.1002/jgrc.20327 CrossRefGoogle Scholar
  43. Rayner NA, Parker D, Horton E, et al. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108. doi: https://doi.org/10.1029/2002JD002670, D14
  44. Rind D, Rossow WB (1984) The effects of physical processes on the Hadley circulation. J Atmos Sci 41(4):479–507.  https://doi.org/10.1175/1520-0469(1984)041 CrossRefGoogle Scholar
  45. Ruiz BA, Nigam S, Kavvada A (2013) The Atlantic multidecadal oscillation in twentieth century climate simulations: uneven progress from CMIP3 to CMIP5. Clim Dyn 41(11-12):3301–3315.  https://doi.org/10.1007/s00382-013-1810-0 CrossRefGoogle Scholar
  46. Schmidt GA, Kelley M, Nazarenko L, Ruedy R, Russell GL, Aleinov I, Bauer M, Bauer SE, Bhat MK, Bleck R, Canuto V, Chen YH, Cheng Y, Clune TL, del Genio A, de Fainchtein R, Faluvegi G, Hansen JE, Healy RJ, Kiang NY, Koch D, Lacis AA, LeGrande AN, Lerner J, Lo KK, Matthews EE, Menon S, Miller RL, Oinas V, Oloso AO, Perlwitz JP, Puma MJ, Putman WM, Rind D, Romanou A, Sato M, Shindell DT, Sun S, Syed RA, Tausnev N, Tsigaridis K, Unger N, Voulgarakis A, Yao MS, Zhang J (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6(1):141–184.  https://doi.org/10.1002/2013MS000265 CrossRefGoogle Scholar
  47. Schneider E, Lindzen RS (1977) Axially symmetric steady state models of the basic state of instability and climate studies. Part I: Linearized calculations. J Atmos Sci 34:253–279.  https://doi.org/10.1175/1520-0469(1977)034 Google Scholar
  48. Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J Clim 21(10):2283–2296.  https://doi.org/10.1175/2007JCLI2100.1 CrossRefGoogle Scholar
  49. Sun Y, Zhou TJ (2014) How does El Niño affect the interannual variability of the boreal summer Hadley circulation? J Clim 27(7):2622–2642.  https://doi.org/10.1175/JCLI-D-13-00277.1 CrossRefGoogle Scholar
  50. Tanaka HL, Ishizaki N, Kitoh A (2004) Trend and interannual variability of Walker, monsoon and Hadley circulations defined by velocity potential in the upper troposphere. Tellus 56A(3):250–269.  https://doi.org/10.1111/j.1600-0870.2004.00049.x CrossRefGoogle Scholar
  51. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498.  https://doi.org/10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  52. Vecchi GA, Soden BJ (2007) Global warming and the weakening of the tropical circulation. J Clim 20(17):4316–4340.  https://doi.org/10.1175/JCLI4258.1 CrossRefGoogle Scholar
  53. Voldoire A et al (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40(9-10):2091–2121.  https://doi.org/10.1007/s00382-011-1259-y CrossRefGoogle Scholar
  54. Watanabe S et al (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4(4):845–872.  https://doi.org/10.5194/gmd-4-845-2011 CrossRefGoogle Scholar
  55. 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(11-12):3127–3143.  https://doi.org/10.1007/s00382-012-1582-y CrossRefGoogle Scholar
  56. Yu YQ, Zheng WP, Wang B et al (2011) Versions g1.0 and g1.1 of the LASG/IAP flexible Global Ocean–Atmosphere–Land System model. Adv Atmos Sci 28(1):99–117.  https://doi.org/10.1007/s00376-010-9112-5 CrossRefGoogle Scholar
  57. Zhang WJ, Li JP, Jin FF (2009) Spatial and temporal features of ENSO meridional scales. Geophys Res Lett 36(15):L15605.  https://doi.org/10.1029/2009GL038672 Google Scholar
  58. Zheng F, Li JP, Clark R, Nnamchi H (2013) Simulation and projection of the southern hemisphere annular mode in CMIP5 models. J Clim 26(24):9860–9879.  https://doi.org/10.1175/JCLI-D-13-00204.1 CrossRefGoogle Scholar
  59. Zheng F, Li JP, Wang L et al (2015) Cross-seasonal influence of the December–February southern hemisphere annular mode on March–May meridional circulation and precipitation. J Clim 28(17):6859–6881.  https://doi.org/10.1175/JCLI-D-14-00515.1 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  2. 2.Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.China-ASEAN Environmental Cooperation CenterBeijingChina
  4. 4.College of Atmospheric SciencesChengdu University of Information TechnologyChengduChina
  5. 5.State key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Personalised recommendations