Abstract
Climate sensitivity represents the response of climate system to doubled CO2 concentration relative to the preindustrial level, which is one of the sources of uncertainty in climate projections. It is unclear how the climate sensitivity and feedbacks will change as a model system is upgraded from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to CMIP6. In this paper, we address this issue by comparing two versions of the Beijing Climate Center Climate System Model (BCC-CSM) participating in CMIP6 and CMIP5, i.e., BCC-CSM2-MR and BCC-CSM1.1m, which have the same horizontal resolution but different physical parameterizations. The results show that the equilibrium climate sensitivity (ECS) of BCC-CSM slightly increases from CMIP5 (2.94 K) to CMIP6 (3.04 K). The small changes in the ECS result from compensation between decreased effective radiative forcing (ERF) and the increased net feedback. In contrast, the transient climate response (TCR) evidently decreases from 2.19 to 1.40 K, nearly the lower bound of the CMIP6 multimodel spread. The low TCR in BCC-CSM2-MR is mainly caused by the small ERF overly even though the ocean heat uptake (OHU) efficiency is substantially improved from that in BCC-CSM1.1m. Cloud shortwave feedback (λSWCL) is found to be the major cause of the increased net feedback in BCC-CSM2-MR, mainly over the Southern Ocean. The strong positive λSWCL in BCC-CSM2-MR is coincidently related to the weakened sea ice-albedo feedback in the same region. This result is caused by reduced sea ice coverage simulated during the preindustrial cold season, which leads to reduced melting per 1-K global warming. As a result, in BCC-CSM2-MR, reduced surface heat flux and strengthened static stability of the planetary boundary layer cause a decrease in low-level clouds and an increase in incident shortwave radiation. This study reveals the important compensation between λSWCL and sea ice-albedo feedback in the Southern Ocean.
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References
Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations. Geophys. Res. Lett., 35, L04802 doi: https://doi.org/10.1029/2007GL032273.
Andrews, T., J. M. Gregory, M. J. Webb, et al., 2012a: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Lett., 39, L09712, doi: https://doi.org/10.1029/2012GL051607.
Andrews, T., J. M. Gregory, P. M. Forster, et al., 2012b: Cloud adjustment and its role in CO2 radiative forcing and climate sensitivity: A review. Surv. Geophys., 33, 619–635, doi: https://doi.org/10.1007/s10712-011-9152-0.
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: https://doi.org/10.1002/wcc.465.
Cess, R. D., M. H. Zhang, G. L. Potter, et al., 1993: Uncertainties in carbon dioxide radiative forcing in atmospheric general circulation models. Science, 262, 1252–1255, doi: https://doi.org/10.1126/sci-ence.262.5137.1252.
Chen, X. L., and T. J. Zhou, 2015: Distinct effects of global mean warming and regional sea surface warming pattern on projected uncertainty in the South Asian summer monsoon. Geophys. Res. Lett., 42, 9433–9439, doi: https://doi.org/10.1002/2015GL066384.
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: https://doi.org/10.1007/s11430-013-4692-4.
Chen, X. L., Z. Guo, T. J. Zhou, et al., 2019: Climate sensitivity and feedbacks of a new coupled model CAMS-CSM to idealized CO2 forcing: A comparison with CMIP5 models. J. Meteor. Res., 33, 31–15, doi: https://doi.org/10.1007/s13351-019-8074-5.
Chung, E. S., and B. J. Soden, 2015: An assessment of methods for computing radiative forcing in climate models. Environ. Res. Lett., 10, 074004 doi: https://doi.org/10.1088/1748-9326/10/7/074004.
Collins, W. D., P. J. Rasch, B. A. Boville, et al., 2004: Description of the NCAR Community Atmosphere Model (CAM3.0). NCAR, Boulder, Colorado, USA, 226 pp.
Cox, P. M., C. Huntingford, and M. S. Williamson, 2018: Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature, 553, 319–233, doi: https://doi.org/10.1038/nature25450.
Eyring, V., S. Bony, G. A. Meehl, et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, doi: https://doi.org/10.5194/gmd-9-1937-2016.
Flato, G., J. Marokzke, B. Abiodum, et al., 2013: Evaluation of climate models. Climate Change 2013: The physical 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.
Forster, P. M., and Taylor K. E., 2006: Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J. Climate, 19, 6181–6194, doi: https://doi.org/10.1175/JCLI3974.1.
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: https://doi.org/10.1029/2003GL018747.
Heinze, C., V. Eyring, P. Friedlingstein, et al., 2019: ESD reviews: Climate feedbacks in the earth system and prospects for their evaluation. Earth Syst. Dyn., 10, 379–452, doi: https://doi.org/10.5194/esd-10-379-2019.
Hu, X. M., P. C. Taylor, M. Cai, et al., 2017: Inter-model warming projection spread: Inherited traits from control climate diversity. Sci. Rep., 7, 4300 doi: https://doi.org/10.1038/s41598-017-04623-7.
Knutti, R., M. A. A. Rugenstein, and G. C. Hegerl, 2017: Beyond equilibrium climate sensitivity. Nat. Geosci., 10, 727–736, doi: https://doi.org/10.1038/ngeo3017.
Le Treut, H., Z. X. Li, and M. Forichon, 1994: Sensitivity of the LMD General Circulation Model to greenhouse forcing associated with two different cloud water parameterizations. J. Climate, 7, 1827–1841, doi: https://doi.org/10.1175/1520-0442(1994)007<1827:SOTLGC>2.0.CO;2.
Li, C., J. S. Von Storch, and J. Marotzke, 2013: Deep-ocean heat uptake and equilibrium climate response. Climate Dyn., 40, 1071–1086, doi: https://doi.org/10.1007/s00382-012-1350-z.
Li, W. P., Y. W. Zhang, X. L. Shi, et al., 2019: Development of land surface model BCC_AVIM2.0 and its preliminary performance in LS3MIP/CMIP6. J. Meteor. Res., 33, 851–869, doi: https://doi.org/10.1007/s13351-019-9016-y.
Liu, C. Y., X. L. Shi, G. Q. Hu, et al., 2019: A simple earth system model for C3IAM: Based on BCC_CSM1.1 and CMIP5 simulations. Nat. Hazards, 99, 1311–1325, doi: https://doi.org/10.1007/s11069-019-03640-1.
Meraner, K., T. Mauritsen, and A. Voigt, 2013: Robust increase in equilibrium climate sensitivity under global warming. Geophys. Res. Lett., 40, 5944–5948, doi: https://doi.org/10.1002/2013GL058118.
Murphy, D. M., and A. R. Ravishankara, 2018: Trends and patterns in the contributions to cumulative radiative forcing from different regions of the world. Pros. Natl. Acad. Sci. USA, 115, 13192–13197, doi: https://doi.org/10.1073/pnas.1813951115.
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: https://doi.org/10.1029/98GL01908.
National Research Council, 1979: Carbon Dioxide and Climate: A Scientific Assessment. Washington D.C., The National Academies Press, 22 pp, doi: https://doi.org/10.17226/12181.
Rugenstein, M., J. Bloch-Johnson, J. Gregory, et al., 2020: Equilibrium climate sensitivity estimated by equilibrating climate models. Geophys. Res. Lett., 47, e2019GL083898 doi: https://doi.org/10.1029/2019GL083898.
Soden, B. J., W. D. Collins, and D. R. Feldman, 2018: Reducing uncertainties in climate models. Implementing accurate calculations of radiative forcing can improve climate projections. Science, 361, 326–327, doi: https://doi.org/10.1126/science.aau1864.
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. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, USA, 1535 pp.
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: https://doi.org/10.1175/BAMS-D-11-00094.1.
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: https://doi.org/10.1007/s00382-013-1725-9.
Wall, C. J., T. Kohyama, and D. L. Hartmann, 2017: Low-cloud, boundary layer, and sea ice interactions over the Southern Ocean during winter. J. Climate, 30, 4857–4871, doi: https://doi.org/10.1175/JCLI-D-16-0483.1.
Watanabe, M., Y. Kamae, M. Yoshimori, et al., 2013: Strengthening of ocean heat uptake efficiency associated with the recent climate hiatus. Geophys. Res. Lett., 40, 3175–3179, doi: https://doi.org/10.1002/grl.50541.
Wu, T. W., 2012: A mass-flux cumulus parameterization scheme for large-scale models: Description and test with observations. Climate Dyn., 38, 725–744, doi: https://doi.org/10.1007/s00382-011-0995-3.
Wu, T. W., W. P. Li, and J. J. Ji, et al., 2013: Global carbon budgets simulated by the Beijing Climate Center climate system model for the last century. J. Geophys. Res. Atmos., 118, 4326–4347, doi: https://doi.org/10.1002/jgrd.50320.
Wu, T. W., L. C. Song, W. P. Li, et al., 2014: An overview of BCC climate system model development and application for climate change studies. J. Meteor. Res., 28, 34–56, doi: https://doi.org/10.1007/s13351-014-3041-7.
Wu, T. W., Y. X. Lu, Y. J. Fang, et al., 2019: The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev., 12, 1573–1600, doi: https://doi.org/10.5194/gmd-12-1573-2019.
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: https://doi.org/10.1007/s13351-015-5036-4.
Zhou, T. J., X. L. Chen, and B. Wu, 2019: Frontier issues on climate change science for supporting Future Earth. Chinese Sci. Bull., 64, 1967–1974, doi: https://doi.org/10.1360/N972018-00818. (in Chinese)
Acknowledgments
We acknowledge the climate modeling groups (in Table 1) for making their model outputs available (https://esgf-node.llnl.gov/projects/cmip6/), and the World Climate Research Program’s (WCRP’s) Working Group on Coupled Modeling (WGCM) for coordinating the CMIP5 and CMIP6.
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Supported by the National Key Research and Development Program of China (2016YFA0602602 and 2017YFA0603503) and National Natural Science Foundation of China (41605057).
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Shi, X., Chen, X., Dai, Y. et al. Climate Sensitivity and Feedbacks of BCC-CSM to Idealized CO2 Forcing from CMIP5 to CMIP6. J Meteorol Res 34, 865–878 (2020). https://doi.org/10.1007/s13351-020-9204-9
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DOI: https://doi.org/10.1007/s13351-020-9204-9