On the role of the stratiform cloud scheme in the inter-model spread of cloud feedback

Journal of Advances in Modeling Earth Systems

Volumn 9, Issue 1, 2017, Pages 423-437

  Geoffroy, O ^a   Sherwood, S C ^a   Fuchs, D ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

climate sensitivity; inter model spread; low cloud cover; radiative feedback; stratiform cloud scheme; stratocumulus

Indexed keywords

BOUNDARY LAYERS; CLIMATE MODELS; CLOUDS; PROBABILITY DENSITY FUNCTION;

CLIMATE SENSITIVITY; INTER-MODEL SPREAD; LOW CLOUD COVER; STRATIFORM CLOUDS; STRATOCUMULUS;

FEEDBACK;

BOUNDARY LAYER; CLIMATE FEEDBACK; CLIMATE MODELING; CLOUD COVER; CLOUD RADIATIVE FORCING; CMIP; COUPLING; FEEDBACK MECHANISM; PROBABILITY DENSITY FUNCTION; RELATIVE HUMIDITY; SENSITIVITY ANALYSIS; STRATIFORM CLOUD; STRATOCUMULUS;

EID: 85013020770     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2016MS000846     Document Type: Article

References (55)

1. Bao, Q., et al. (2013), The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALS-s2, Adv. Atmos. Sci., 30(3), 561–576, doi:10.1007/s00376-012-2113-9.
2. Bentsen, M., et al. (2012), The Norwegian earth system model, NorESM1-M-Part 1: Description and basic evaluation, Geosci. Model Dev. Discuss., 5, 2843–2931.
3. Betts, A. K., and Harshvardhan (1987), Thermodynamic constraint on the cloud liquid water feedback in climate models, J. Geophys. Res., 92, 8483–8485, doi:10.1029/JD092iD07p08483.
4. Bony S., and K. Emanuel (2001), A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data, J. Atmos. Sci., 58(21), 3158–3183.
5. Bretherton, C. S. (2015), Insights into low-latitude cloud feedbacks from high-resolution models, Philos. Trans. R. Soc. A, 373, 20140415. [Available at http://dx.doi.org/10.1098/rsta.2014.0415.]
6. Bretherton, C. S., P. N. Blossey, and C. R. Jones (2013), Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases, J. Adv. Model. Earth Syst., 5, 316–337, doi:10.1002/jame.20019.
7. Brient, F., and S. Bony (2012), How may low-cloud radiative properties simulated in the current climate influence low-cloud feedbacks under global warming?, Geophys. Res. Lett., 39, L20807, doi:10.1029/2012GL053265.
8. Chylek, P., J. Li, M. K. Dubey, M. Wang, and G. Lesins (2011), Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2, Atmos. Chem. Phys. Discuss., 11, 22,893–22,907.
9. Collins, W. D., et al. (2004), Description of the NCAR community atmosphere model (CAM 3.0), NCAR Tech. Note NCAR/TN-464+ STR, 226 pp., Natl. Cent. for Atmos. Res., Boulder, Colo.
10. Dufresne, J.-L., and S. Bony (2008), An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models, J. Atmos. Sci., 21, 5135–5144.
11. Dufresne, J. L., et al. (2013), Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5, Clim. Dyn., 40(9-10), 2123–2165.
12. Fushan, D. A. I., Y. Rucong, X. Zhang, and Y. Yu (2005), A statistically-based low-level cloud scheme and its tentative application in a general circulation model, Acta Meteorol. Sin., 19(3), 263.
13. Geoffroy, O., D.Saint-Martin, D.J. Olivié, A. Voldoire, G. Bellon, and S. Tytéca (2013), Transient climate response in a two-layer energy-balance model. Part I: Analytical solution and parameter calibration using CMIP5 AOGCM experiments, J. Clim., 26, 1841–1857.
14. Gettelman, A., J. E. Kay, and K. M. Shell (2012), The evolution of climate sensitivity and climate feedbacks in the community atmosphere model, J. Clim., 25, 1453–1469.
15. Gordon, H. B. et al., (2002), The CSIRO Mk3 Climate System Model, Tech. Pap. 60, CSIRO Atmos. Res., Aspendale, Victoria, Australia. [Available at http://www.cmar.csiro.au/e-print/open/Gordon 2002a.pdf.]
16. Gregory, D., and P. R. Rowntree (1990), A mass flux convection scheme with representation of cloud ensemble characteristics and stability dependent closure, Mon. Weather Rev., 118, 1483–1506.
17. Gregory, J. M., W. J. Ingram, M. A. Palmer, G. S. Jones, P. A. Stott, R. B. Thorpe, J. A. Lowe, T. C. Johns, and K. D. Williams (2004), A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747.
18. Hack, J. J. (1994), Parameterization of moist convection in the National Center for Atmospheric Research Community Climate Model (CCM2), J. Geophys. Res., 99, 5551–5568.
19. Hartmann, D. L., and K. Larson (2002), An important constraint on tropical cloudclimate feedback, Geophys. Res. Lett., 29, 1951, doi:10.1029/2002GL015835.
20. Holtslag, A. A. M., and C.-H. Moeng (1991), Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer, J. Atmos. Sci., 48, 1690–1698.
21. Hourdin, F., et al. (2006), The LMDZ4 general circulation model: Climate performance and sensitivity to parametrized physics with emphasis on tropical convection, Clim. Dyn., 27, 787–813, doi:10.1007/s00382-006-0158-0.
22. Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch (1996), Description of the NCAR Community Climate Model (CCM3), NCAR Tech. Note NCAR/TN-420+STR, Natl. Cent. for Atmos. Res., Boulder, Colo. doi:10.5065/D6FF3Q99.
23. Le Treut, H., and Z.-X. Li (1991), Sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties, Clim. Dyn., 5, 175–187.
24. Li, L., et al. (2013), The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2, Adv. Atmos. Sci., 30, 543–560.
25. Liu, H., X. Zhang, and G. Wu (1998), Cloud feedback on SST variability in the western equatorial Pacific in GOALS/LASG model, Adv. Atmos. Sci., 15(3), 412–423.
26. Martin, G. M., et al. (2011), The HadGEM2 family of Met Office Unified Model climate configurations, Geosci. Model Dev., 4, 723–757, doi:10.5194/gmd-4-723-2011.
27. McFarlane, N. A., G. J. Boer, J-P. Blanchet, and M. Lazare (1992), The Canadian Climate Centre second-generation general circulation model and its equilibrium climate, J. Clim., 5, 1013–1044.
28. Meehl, G. A., C. Covey, and K. E. Taylor (2007), The WCRP CMIP3 multimodel dataset: A new era in climate change research, Bull. Am. Meteorol. Soc., 88, 1383–1394.
29. Neale, R. B., et al. (2010), Description of the NCAR community atmosphere model (CAM 4.0), technical report, NCAR, Natl. Cent. for Atmos. Res., Boulder, Colo.
30. Phipps, S. J., L. D. Rotstayn, H. B. Gordon, J. L. Roberts, A.C. Hirst, and W. F. Budd (2011), The CSIRO-Mk3L climate system model version 1.0 – Part 1: Description and evaluation, Geosci. Model Dev., 4, 483–509.
31. Qu X., A. Hall, S. A. Klein, and P. M. Caldwell (2014), On the spread of changes in marine low cloud cover in climate model simulations of the 21st century, Clim. Dyn., 42(9-10), 2603–2626.
32. Rieck M., L. Nuijens, and B. Stevens (2012), Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere, J. Atmos. Sci., 69(8), 2538–2550.
33. Ringer, M. A., T. Andrews, and M. J. Webb (2014), Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments, Geophys. Res. Lett., 41, 4035–4042, doi:10.1002/2014GL060347.
34. Rotstayn, L. D. (1997), A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models, I: Description and evaluation of the microphysical processes, Q. J. R. Meteorol. Soc., 123, 1227–1282, doi:10.1002/qj.49712354106.
35. Rotstayn, L. D., M. A. Collier, M. R. Dix, Y. Feng, H. B. Gordon, S. P. O'Farrell, I. N. Smith, and J. Syktus (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.
36. Scinocca, J. F., N. A. McFarlane, M. Lazare, J. Li, and D. Plummer (2008), Technical Note: The CCCma third generation AGCM and its extension into the middle atmosphere, Atmos. Chem. Phys., 8, 7055–7074, doi:10.5194/acp-8-7055-2008.
37. Sherwood, S. C., W. Ingram, Y. Tsushima, M. Satoh, M. Roberts, P. L. Vidale, and P. A. O'Gorman (2010), Relative humidity changes in a warmer climate, J. Geophys. Res., 115, D09104, doi:10.1029/2009JD012585.
38. Sherwood, S. C., S. Bony, and J. Dufresne (2014), Spread in model climate sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42.
39. Slingo, J. M. (1987), The development and verification of a cloud prediction scheme for the ECMWF model, Q. J. R. Meteorol. Soc., 113, 899–927.
40. Smith, R. N. B. (1990), A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. R. Meteorol. Soc., 116,435- 460.
41. Soden, B. J., and I. M. Held (2006), An assessment of climate feedbacks in coupled ocean-atmosphere models, J. Clim., 19, 3354–3360.
42. Stainforth, D. A., et al. (2005), Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406.
43. Taylor, K. E, Ronald, J. Stouffer, and G. A. Meehl (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93(4), 485–498.
44. Volodin, E. M. (2014), Possible reasons for low climate-model sensitivity to increased carbon dioxide concentrations, Izv. Atmos. Oceanic Phys., 50(4), 350–355.
45. Volodin, E. M., N. A. Dianskii, and A. V. Gusev (2010), Simulating present-day climate with the INMCM4. 0 coupled model of the atmospheric and oceanic general circulations, Izv. Atmos. Oceanic Phys., 46(4), 414–431.
46. Washington, W. M., et al. (2000), Parallel climate model (PCM) control and transient simulations, Clim. Dyn., 16(10-11), 755–774.
47. Watanabe, S., et al. (2011), MIROC-ESM: Model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev. Discuss., 4(2), 1063–1128.
48. Webb, M. J., et al. (2015), The impact of parametrized convection on cloud feedback, Philos. Trans. R. Soc. A, 373, 2054.
49. Wetherald, R. T., and S. Manabe (1980), Cloud cover and climate sensitivity, J. Atmos. Sci., 37, 1485–1510.
50. Wu, T., R. Yu, F. Zhang, Z. Wang, M. Dong, L. Wang, X. Jin, D. Chen, and L. Li (2010), The Beijing Climate Center atmospheric general circulation model: Description and its performance for the present-day climate, Clim. Dyn., 34(1), 123–147.
51. Xu, K. M., and D. A. Randall (1996), A semiempirical cloudiness parameterization for use in climate models, J. Atmos. Sci., 53(21), 3084–3102.
52. Zelinka, M. D., C. Zhou, and S. A. Klein (2016), Insights from a refined decomposition of cloud feedbacks, Geophys. Res. Lett., 43, 9259–9269, doi:10.1002/2016GL069917.
53. Zhang, G. J., and N. A. McFarlane (1995), Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model, Atmosphere-Ocean, 33, 407–446.
54. Zhang, M., et al. (2013), CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models, J. Adv. Model. Earth Syst., 5, 826–842, doi:10.1002/2013MS000246.
55. Zhao, M., et al. (2016), Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics, J. Clim., 29(2), 543–560.