Advertisement

Journal of Meteorological Research

, Volume 33, Issue 1, pp 31–45 | Cite as

Climate Sensitivity and Feedbacks of a New Coupled Model CAMS-CSM to Idealized CO2 Forcing: A Comparison with CMIP5 Models

  • Xiaolong ChenEmail author
  • Zhun Guo
  • Tianjun Zhou
  • Jian Li
  • Xinyao Rong
  • Yufei Xin
  • Haoming Chen
  • Jingzhi Su
Special Collection on CAMS-CSM
  • 3 Downloads

Abstract

Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences (CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5 (CMIP5). Based on two idealized CO2 forcing scenarios, i.e., abruptly quadrupled CO2 and CO2 increasing 1% per year, the equilibrium climate sensitivity (ECS) and transient climate response (TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean (MME) of CMIP5 due to compensation of a relatively low ocean heat uptake (OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback (λSWCL) over the tropical Indo-Pacific Ocean.

The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level (925–700 hPa) cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.

Key words

climate sensitivity climate feedback cloud shortwave feedback the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM) Coupled Model Comparison Project phase 5 (CMIP5) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We acknowledge the climate modeling groups (listed in Table 1) for making their model outputs available (https://doi.org/www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html), and the World Climate Research Program’s (WCRP’s) Working Group on Coupled Modeling (WGCM) for coordinating the CMIP5 project.

References

  1. Andrews, T., J. M. Gregory, M. J. Webb, et al., 2012: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models. Geophys. Res. Lett., 39, L09712, doi: 10.1029/2012GL051607.Google Scholar
  2. Boucher, O., D. Randall, P. Artaxo, et al., 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. H. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 1535 pp.Google Scholar
  3. Bretherton, C. S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philos. Trans. Roy. Soc. A: Math., Phys. Eng. Sci., 373: 20140415, doi: 10.1098/rsta.2014.0415.CrossRefGoogle Scholar
  4. Ceppi, P., D. L. Hartmann, and M. J. Webb, 2016: Mechanisms of the negative shortwave cloud feedback in middle to high latitudes. J. Climate, 29: 139–157, doi: 10.1175/JCLI-D-15-0327.1.CrossRefGoogle Scholar
  5. Ceppi, P., F. Brient, M. D. Zelinka, et al., 2017: Cloud feedback mechanisms and their representation in global climate models. WIREs Climate Change, 8, e465, doi: 10.1002/wcc.465.CrossRefGoogle Scholar
  6. Charney, J. G., A. Arakawa, D. J. Baker, et al., 1979: Carbon Dioxide and Climate: A Scientific Assessment. Report of an Ad Hoc Study Group on Carbon Dioxide and Climate. National Academy of Sciences Press, Washington D.C., 22 pp, doi: 10.17226/12181.Google Scholar
  7. Chen, X. L., T. J. Zhou, and Z. Guo, 2014: Climate sensitivities of two versions of FGOALS model to idealized radiative forcing. Sci. China Earth Sci., 57: 1363–1373, doi: 10.1007/s11430-013-4692-4.CrossRefGoogle Scholar
  8. Cox, P. M., C. Huntingford, and M. S. Williamson, 2018: Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature, 553: 319–322, doi: 10.1038/nature25450.CrossRefGoogle Scholar
  9. Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84: 1013–1024, doi: 10.1175/BAMS-84-8-1013.CrossRefGoogle Scholar
  10. Flato, G., J. Marotzke, B. Abiodun, et al., 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. H. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 1535 pp.Google Scholar
  11. Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component in CO2 forcing. J. Climate, 21: 58–71, doi: 10.1175/2007JCLI1834.1.CrossRefGoogle Scholar
  12. Gregory, J. M., R. J. Stouffer, S. C. B. Raper, et al., 2002: An observationally based estimate of the climate sensitivity. J. Climate, 15: 3117–3121, doi: 10.1175/1520-0442(2002)015<31 17:AOBEOT>2.0.CO;2.CrossRefGoogle Scholar
  13. Gregory, J. M., W. J. Ingram, M. A. Palmer, et al., 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi: 10.1029/2003 GL018747.Google Scholar
  14. Hansen, J., A. Lacis, D. Rind, et al., 1984: Climate sensitivity: Analysis of feedback mechanisms. Climate Processes and Climate Sensitivity, J. E. Hansen, and T. Takahashi, Eds., American Geophysical Union, Washington D.C., 130–163.CrossRefGoogle Scholar
  15. Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25: 441–475, doi: 10.1146/annurev.energy.25.1.441.CrossRefGoogle Scholar
  16. Knutti, R., M. A. A. Rugenstein, and G. C. Hegerl, 2017: Beyond equilibrium climate sensitivity. Nat. Geosci., 10: 727–736, doi: 10.1038/ngeo3017.CrossRefGoogle Scholar
  17. Li, C., J. S. Von Storch, and J. Marotzke, 2013: Deep-ocean heat uptake and equilibrium climate response. Climate Dyn., 40: 1071–1086, doi: 10.1007/s00382-012-1350-z.CrossRefGoogle Scholar
  18. Li, J., H. M. Chen, X. Y. Rong, et al., 2018: How well can a climate model simulate an extreme precipitation event: A case study using the Transpose-AMIP experiment. J. Climate, 31: 6543–6556, doi: 10.1175/JCLI-D-17-0801.1.CrossRefGoogle Scholar
  19. Meraner, K., T. Mauritsen, and A. Voigt, 2013: Robust increase in equilibrium climate sensitivity under global warming. Geophys. Res. Lett., 40: 5944–5948, doi: 10.1002/2013GL 058118.CrossRefGoogle Scholar
  20. Murray, R. J., 1996: Explicit generation of orthogonal grids for ocean models. J. Comput. Phys., 126: 251–273, doi: 10.1006/jcph.1996.0136.CrossRefGoogle Scholar
  21. Myhre, G., E. J. Highwood, K. P. Shine, et al., 1998: New estimates of radiative forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25: 2715–2718, doi: 10.1029/98GL 01908.CrossRefGoogle Scholar
  22. Qu, X., A. Hall, S. A. Klein, et al., 2014: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42: 2603–2626, doi: 10.1007/s00382-013-1945-z.CrossRefGoogle Scholar
  23. Randall, D. A., R. A. Wood, S. Bony, et al., 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, D. H. Qin, M. Manning, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 996 pp.Google Scholar
  24. Rieck, M., L. Nuijens, and B. Stevens, 2012: Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere. J. Atmos. Sci., 69: 2538–2550, doi: 10.1175/JAS-D-11-0203.1.CrossRefGoogle Scholar
  25. Roe, G., 2009: Feedbacks, timescales, and seeing red. Annu. Rev. Earth Planet Sci., 37: 93–115, doi: 10.1146/annurev.earth. 061008.134734.CrossRefGoogle Scholar
  26. Roeckner, E., U. Schlese, J. Biercamp, et al., 1987: Cloud optical depth feedbacks and climate modelling. Nature, 329: 138–140, doi: 10.1038/329138a0.CrossRefGoogle Scholar
  27. Roeckner, E., G. Bäuml, L. Bonaventura, et al., 2003: The Atmospheric General Circulation Model ECHAM5. Part I: Model Description. Report No. 349, Max Planck Institute for Meteorology, Hamburg, Germany, 127 pp.Google Scholar
  28. Rong, X. Y., J. Li, H. M. Chen, et al., 2018: The CAMS Climate System Model and a basic evaluation of its climatology and climate variability simulation. J. Meteor. Res., 32: 839–861, doi: 10.1007/s13351-018-8058-x.CrossRefGoogle Scholar
  29. Sherwood, S. C., S. Bony, and J. L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505: 37–42, doi: 10.1038/nature12829.CrossRefGoogle Scholar
  30. Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model. Quart. J. Roy. Meteor. Soc., 113: 899–927, doi: 10.1002/qj.49711347710.CrossRefGoogle Scholar
  31. Soden, B. J., A. J. Broccoli, and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17: 3661–3665, doi: 10.1175/1520-0442(2004)017<3661:OTUO CF>2.0.CO;2.CrossRefGoogle Scholar
  32. Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18: 237–273, doi: 10.1175/JCLI-3243.1.CrossRefGoogle Scholar
  33. Stocker, T. F., D. H. Qin, G. K. Plattner, et al., 2013: Technical summary. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker, D. H. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 1535 pp.Google Scholar
  34. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93: 485–498, doi: 10.1175/BAMS-D-11-00094.1.CrossRefGoogle Scholar
  35. Vial, J., J. L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn., 41: 3339–3362, doi: 10.1007/s00382-013-1725-9.CrossRefGoogle Scholar
  36. Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol., 17: 525–531, doi: 10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.CrossRefGoogle Scholar
  37. Yu, R. C., 1994: A two-step shape-preserving advection scheme. Adv. Atmos. Sci., 11: 479–490, doi: 10.1007/BF02658169.CrossRefGoogle Scholar
  38. Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. J. Climate, 25: 3736–3754, doi: 10.1175/JCLI-D-11-00249.1.Google Scholar
  39. Zhang, H., G. Y. Shi, T. Nakajima, et al. 2006a: The effects of the choice of the k-interval number on radiative calculations. J. Quant. Spectros. Radiat. Trans., 98: 31–43, doi: 10.1016/j.jqsrt.2005.05.090.Google Scholar
  40. Zhang, H., T. Suzuki, T. Nakajima, et al., 2006b: Effects of band division on radiative calculations. Opt. Eng., 45: 016002, doi: 10.1117/1.2160521.Google Scholar
  41. Zhou, T. J., and X. L. Chen, 2015: Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback. J. Meteor. Res., 29: 884–895, doi: 10.1007/s13351-015-5036-4.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiaolong Chen
    • 1
    Email author
  • Zhun Guo
    • 2
    • 1
  • Tianjun Zhou
    • 1
    • 3
  • Jian Li
    • 4
  • Xinyao Rong
    • 4
  • Yufei Xin
    • 4
  • Haoming Chen
    • 4
  • Jingzhi Su
    • 4
  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Climate Change Research Center, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.University of the Chinese Academy of SciencesBeijingChina
  4. 4.State Key Laboratory of Severe WeatherChinese Academy of Meteorological Sciences, China Meteorological AdministrationBeijingChina

Personalised recommendations