Advertisement

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

  • Jessica VialEmail author
  • Sandrine Bony
  • Bjorn Stevens
  • Raphaela Vogel
Chapter
Part of the Space Sciences Series of ISSI book series (SSSI, volume 65)

Abstract

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and highresolution modeling on large domains are discussed.

Keywords

Climate sensitivity Global climate models High-resolution Models Low-cloud feedbacks Observations Single-column models Trade-wind shallow cumulus clouds 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Albrecht BA, Betts AK, Schubert WH, Cox SK (1979) Model of the thermodynamic structure of the tradewind boundary layer: Part I. Theoretical formulation and sensitivity tests. J Atmos Sci 36(1):73–89Google Scholar
  2. Bellon G, Stevens B (2012) Using the sensitivity of large-eddy simulations to evaluate atmospheric boundary layer models. J Atmos Sci 69(5):1582–1601Google Scholar
  3. Bellon G, Stevens B (2013) Time scales of the trade wind boundary layer adjustment. J Atmos Sci 70(4):1071–1083Google Scholar
  4. Betts AK (1976) Modeling subcloud layer structure and interaction with a shallow cumulus layer. J Atmos Sci 33(12):2363–2382Google Scholar
  5. Betts AK, Ridgway W (1989) Climatic equilibrium of the atmospheric convective boundary layer over a tropical ocean. J Atmos Sci 46(17):2621–2641Google Scholar
  6. Blossey PN, Bretherton CS, Zhang M, Cheng A, Endo S, Heus T, Liu Y, Lock AP, Roode SR, Xu KM (2013) Marine low cloud sensitivity to an idealized climate change: the cgils les intercomparison. J Adv Model Earth Syst 5(2):234–258Google Scholar
  7. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32(20):L20806Google Scholar
  8. Bony S, Stevens B, Ament F, Bigorre S, Chazette P, Crewell S, Delanoë J, Emanuel K, Farrell D, Flamant C, Gross S, Hirsch L, Karstensen J, Mayer B, Nuijens L, Ruppert Jr JH, Sandu I, Siebesma P, Speich S, Szczap F, Totems J, Vogel R, Wendisch M, Wirth M (2017) EUREC4A: a field campaign to elucidate the couplings between clouds, convection and circulation. Surv Geophys (in revision)Google Scholar
  9. Boucher O, Randall D, Artaxo P, Bretherton C, Feingold G, Forster P, Kerminen VM, Kondo Y, Liao H, Lohmann U, et al (2013) Clouds and aerosols. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, vol 5, Cambridge University Press, Cambridge, pp 571–657Google Scholar
  10. Bretherton CS (2015) Insights into low-latitude cloud feedbacks from high-resolution models. Philos Trans R Soc A 373(2054):20140415Google Scholar
  11. Bretherton CS, Blossey PN, Jones CR (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(2):316–337Google Scholar
  12. Brient F, Bony S (2012) How may low-cloud radiative properties simulated in the current climate influence low-cloud feedbacks under global warming? Geophys Res Lett 39(20):L20807Google Scholar
  13. Brient F, Bony S (2013) Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Clim Dyn 40(9–10):2415–2431Google Scholar
  14. Brient F, Schneider T (2016) Constraints on climate sensitivity from space-based measurements of lowcloud reflection. J Clim 29:5821–5835Google Scholar
  15. Brient F, Schneider T, Tan Z, Bony S, Qu X, Hall A (2015) Shallowness of tropical low clouds as a predictor of climate models response to warming. Clim Dyn 47(1):433–449Google Scholar
  16. Brown A (1999) Large-eddy simulation and parametrization of the effects of shear on shallow cumulus convection. Bound Layer Meteorol 91(1):65–80Google Scholar
  17. Brown A, Cederwall R, Chlond A, Duynkerke P, Golaz JC, Khairoutdinov M, Lewellen D, Lock A, MacVean M, Moeng CH et al (2002) Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land. Q J R Meteorol Soc 128(582):1075–1093Google Scholar
  18. Clement AC, Burgman R, Norris JR (2009) Observational and model evidence for positive low-level cloud feedback. Science 325(5939):460–464Google Scholar
  19. Heinze R, Dipankar A, Carbajal Henken C, Moseley C, Sourdeval O, Trömel S, Xie X, Adamidis P, Ament F, Baars H, et al (2016) Large-eddy simulations over Germany using ICON: a comprehensive evaluation. Q J R Meteorol Soc 143(702):69–100Google Scholar
  20. Kamae HY, Shiogama M, Watanabe T, Ogura T, Yokohata T, Kimoto M (2016) Lower tropospheric mixing as a constraint on cloud feedback in a multi-parameter multi-physics ensemble. J Clim.  https://doi.org/10.1175/JCLI-D-16-0042.1
  21. Klein SA, Hall A (2015) Emergent constraints for cloud feedbacks. Curr Clim Change Rep 1(4):276–287Google Scholar
  22. Matheou G, Chung D, Nuijens L, Stevens B, Teixeira J (2011) On the fidelity of large-eddy simulation of shallow precipitating cumulus convection. Monthly Weather Rev 139(9):2918–2939Google Scholar
  23. Medeiros B, Stevens B, Bony S (2015) Using aquaplanets to understand the robust responses of comprehensive climate models to forcing. Clim Dyn 44(7–8):1957–1977Google Scholar
  24. Neggers R (2015a) Attributing the behavior of low-level clouds in large-scale models to subgrid-scale parameterizations. J Adv Model Earth Syst 7(4):2029–2043Google Scholar
  25. Neggers R (2015b) Exploring bin-macrophysics models for moist convective transport and clouds. J Adv Model Earth Syst 7(4):2079–2104Google Scholar
  26. Neggers R, Stevens B, Neelin JD (2006) A simple equilibrium model for shallow-cumulus-topped mixed layers. Theor Computat Fluid Dyn 20(5–6):305–322Google Scholar
  27. Nuijens L, Serikov I, Hirsch L, Lonitz K, Stevens B (2014) The distribution and variability of low-level cloud in the North Atlantic trades. Q J R Meteorol Soc 140(684):2364–2374Google Scholar
  28. Nuijens L, Medeiros B, Sandu I, Ahlgrimm M (2015a) The behavior of trade-wind cloudiness in observations and models: the major cloud components and their variability. J Adv Model Earth Syst 7(2):600–616Google Scholar
  29. Nuijens L, Medeiros B, Sandu I, Ahlgrimm M (2015b) Observed and modeled patterns of covariability between low-level cloudiness and the structure of the trade-wind layer. J Adv Model Earth Syst 7(4):1741–1764Google Scholar
  30. Qu X, Hall A, Klein SA, Caldwell PM (2014) On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Clim Dyn 42(9–10):2603–2626Google Scholar
  31. Qu X, Hall A, Klein SA, DeAngelis AM (2015) Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys Res Lett 42(18):7767–7775Google Scholar
  32. Rieck M, Nuijens L, Stevens B (2012) Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere. J Atmos Sci 69(8):2538–2550Google Scholar
  33. Seifert A, Heus T (2013) Large-eddy simulation of organized precipitating trade wind cumulus clouds. Atmos Chem Phys 13(11):5631–5645Google Scholar
  34. Seifert A, Heus T, Pincus R, Stevens B (2015) Large-eddy simulation of the transient and near-equilibrium behavior of precipitating shallow convection. J Adv Model Earth Syst 7(4):1918–1937Google Scholar
  35. Sherwood SC, Bony S, Dufresne JL (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505(7481):37–42.  https://doi.org/10.1038/nature12829
  36. Siebesma AP, Bretherton CS, Brown A, Chlond A, Cuxart J, Duynkerke PG, Jiang H, Khairoutdinov M, Lewellen D, Moeng CH et al (2003) A large eddy simulation intercomparison study of shallow cumulus convection. J Atmos Sci 60(10):1201–1219Google Scholar
  37. Stevens B (2006) Bulk boundary-layer concepts for simplified models of tropical dynamics. Theor Comput Fluid Dyn 20(5–6):279–304Google Scholar
  38. Stevens B (2007) On the growth of layers of nonprecipitating cumulus convection. J Atmos Sci 64:2916–2931Google Scholar
  39. Stevens B, Seifert R (2008) Understanding macrophysical outcomes of microphysical choices in simulations of shallow cumulus convection. J Meteorol Soc Japan 86:143–162Google Scholar
  40. Stevens B, Ackerman AS, Albrecht BA, Brown AR, Chlond A, Cuxart J, Duynkerke PG, Lewellen DC, Macvean MK, Neggers RA et al (2001) Simulations of trade wind cumuli under a strong inversion. J Atmos Sci 58(14):1870–1891Google Scholar
  41. Stevens B, Farrell D, Hirsch L, Jansen F, Nuijens L, Serikov I, Brügmann B, Forde M, Linne H, Lonitz K et al (2016) The barbados cloud observatory-anchoring investigations of clouds and circulation on the edge of the ITCZ. Bull Am Meteorol Soc 97:787–801Google Scholar
  42. Tan Z, Schneider T, Teixeira J, Pressel KG (2017) Large-eddy simulation of subtropical cloud-topped boundary layers: 2. Cloud response to climate change. J Adv Model Earth Syst 9(1):19–38Google Scholar
  43. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of cmip5 and the experiment design. Bull Am Meteorol Soc 93(4):485Google Scholar
  44. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Rev 117(8):1779–1800Google Scholar
  45. Tomassini L, Voigt A, Stevens B (2014) On the connection between tropical circulation, convective mixing, and climate sensitivity. Q J R Meteorol Soc 141(689):1404–1416Google Scholar
  46. Van Zanten MC, Stevens B, Nuijens L, Siebesma AP, Ackerman A, Burnet F, Cheng A, Couvreux F, Jiang H, Khairoutdinov M et al (2011) Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO. J Adv Model Earth Syst 3(2):M06001Google Scholar
  47. Vial J, Bony S, Dufresne JL, Roehrig R (2016) Coupling between lower-tropospheric convective mixing and low-level clouds: physical mechanisms and dependence on convection scheme. JAMES.  https://doi.org/10.1002/2016MS000740
  48. Vogel R, Nuijens L, Stevens B (2016) The role of precipitation and spatial organization in the response of trade-wind clouds to warming. J Adv Model Earth Syst 8:843–862Google Scholar
  49. Watanabe M, Shiogama H, Yokohata T, Kamae Y, Yoshimori M, Ogura T, Annan JD, Hargreaves JC, Emori S, Kimoto M (2012) Using a multiphysics ensemble for exploring diversity in cloud-shortwave feedback in GCMs. J Clim 25(15):5416–5431Google Scholar
  50. Webb M, Andrews T, Bodas-Salcedo A, Bony S, Bretherton C, Chadwick R, Chepfer H, Douville H, Good P, Kay J, et al (2016) The cloud feedback model intercomparison project (CFMIP) contribution to CMIP6 . Geoscientific Model Development Discussions 2016: in-openGoogle Scholar
  51. Webb MJ, Lock AP (2013) Coupling between subtropical cloud feedback and the local hydrological cycle in a climate model. Clim Dyn 41(7–8):1923–1939Google Scholar
  52. Webb MJ, Senior C, Sexton D, Ingram W, Williams K, Ringer M, McAvaney B, Colman R, Soden B, Gudgel R et al (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27(1):17–38Google Scholar
  53. Webb MJ, Lock AP, Bretherton CS, Bony S, Cole JN, Idelkadi A, Kang SM, Koshiro T, Kawai H, Ogura T, Roehrig R, Shin Y, Mauritsen T, Sherwood SC, Vial J, Watanabe M, Woelfle MD, Zhao M (2015) The impact of parametrized convection on cloud feedback. Philos Trans R Soc A 373(2054):20140414Google Scholar
  54. Wing AA, Emanuel KA, Holloway CE, Muller C (2017) Convective self-aggregation in numerical simulations: a review. Surv Geophys Rev.  https://doi.org/10.1007/s10712-017-9408-4
  55. Wyant MC, Bretherton CS, Blossey PN (2009) Subtropical low cloud response to a warmer climate in a superparameterized climate model. Part I: regime sorting and physical mechanisms. J Adv Model Earth Syst.  https://doi.org/10.3894/JAMES.2009.1.7
  56. Zängl G, Reinert D, Rípodas P, Baldauf M (2015) The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: description of the non-hydrostatic dynamical core. Q J R Meteorol Soci 141(687):563–579Google Scholar
  57. Zelinka MD, Zhou C, Klein SA (2016) Insights from a refined decomposition of cloud feedbacks. Geophys Res Lett 43(17):9259–9269Google Scholar
  58. Zhang M, Bretherton CS, Blossey PN, Bony S, Brient F, Golaz JC (2012) The CGILS experimental design to investigate low cloud feedbacks in general circulation models by using single-column and largeeddy simulation models. J Adv Model Earth Syst.  https://doi.org/10.1029/2012MS000182
  59. Zhang M, Bretherton CS, Blossey PN, Austin PH, Bacmeister JT, Bony S, Brient F, Cheedela SK, Cheng A, Genio AD 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(4):826–842Google Scholar
  60. Zhao M (2014) An investigation of the connections among convection, clouds, and climate sensitivity in a global climate model. J Clim 27(5):1845–1862Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Jessica Vial
    • 1
    Email author
  • Sandrine Bony
    • 2
  • Bjorn Stevens
    • 3
  • Raphaela Vogel
    • 3
  1. 1.Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN)Université Pierre et Marie CurieParis, Cedex 05France
  2. 2.Laboratoire de Météorologie Dynamique (LMD)CNRS, Université Pierre et Marie CurieParis, Cedex 05France
  3. 3.Max Planck Institute for Meteorology (MPI)HamburgGermany

Personalised recommendations