Paper Reviewed
Qu, X., Hall, A., Klein, S.A. and Caldwell, P.M. 2015. The strength of the tropical inversion and its response to climate change in 18 CMIP5 models. Climate Dynamics 45: 375-396.

Qu et al. (2015) indicate that "the lowest few kilometers of the tropical marine atmosphere are frequently capped by an inversion layer, characterized by a large temperature and/or moisture jump," under which inversion "lie several types of boundary layer clouds" -- as noted by Klein and Hartmann (1993), Betts (1997), Moeng and Stevens (1999), Stevens (2005) and Wood (2012)" -- which via their effects on net incoming shortwave radiation, "play a critical role in regulating the global energy budget," citing Hartmann et al. (1992) and Chen et al. (2000).

In addition, they write that "changes in these clouds associated with simulated anthropogenic climate change likewise have a large effect on the shortwave component of the anthropogenic perturbation to the global energy budget," citing the studies of Slingo (1990), Bony and Dufresne (2005), Stephens (2005), Soden and Held (2006), Williams et al. (2006), Wyant et al. (2006), Webb et al. (2012) and Zelinka et al. (2012), while noting, in their words, that these changes "remain the major source of uncertainty surrounding climate sensitivity," citing Webb et al. (2012) and Vial et al. (2013).

In exploring this subject in more detail, Qu et al. went on to examine the tropical inversion strength as measured by "the estimated inversion strength (EIS), and its response to climate change in 18 models associated with phase 5 of the coupled model intercomparison project (CMIP5)." And what did they thereby learn?

The four U.S. researchers report that the CMIP5 models (1) "systematically underestimate present-day EIS off the west coasts of subtropical continents," that (2) "this bias is largely attributable to the positive SST bias commonly seen in the fully coupled atmosphere-ocean simulations," and that (3) "estimates of the inversion changes are somewhat sensitive to [the assumption that] surface relative humidity holds to a constant value (80%)." In addition, they state that "several aspects of the EIS behavior have not yet been fully understood, including (1) what drives the deviation from the moist adiabat in the warm pools, (2) what processes are most responsible for the uneven warming between the warm pools and the subtropical ocean, and (3) what drives the intermodal spread in the magnitude of EIS increase."

And so it is that the three U.S. researchers ultimately conclude that "further work is necessary to answer these questions, and to have confidence in the model projections of EIS increase," and, we would add, in the predicted effects of the model projections on those pesky boundary layer clouds.

References
Betts, A.K. 1997. The Physics and Parameterization of Moist Atmospheric Convection. Chapter 4: Trade Cumulus: Observations and Modelling. Kluwer, Dordrecht, the Netherlands.

Bony, S. and Dufresne, J.L. 2005. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophysical Research Letters 32: 10.1029/2005GL023851.

Chen, J., Rossow, W.B. and Zhang, Y. 2000. Radiative effects of cloud-type variations. Journal of Climate 13: 264-286.

Hartmann, D.L., Ockert-Bell, M.E. and Michelsen, M.L. 1992. The effect of cloud type on earth's energy balance: global analysis. Journal of Climate 5: 1281-1304.

Klein, S.A. and Hartmann, D.L. 1993. The seasonal cycle of low stratiform clouds. Journal of Climate 6: 1587-1606.

Moeng, C.H. and Stevens, B. 1999. Marine stratocumulus and its representation in GCMs. In: Randall, D.A. (Ed.). General Circulation Model Development: Past, Present, and Future. Elsevier, New York, New York, USA, pp. 577-604.

Slingo, A. 1990. Sensitivity of the earth's radiation budget to changes in low clouds. Nature 343: 49-51.

Soden, B.J. and Held, I.M. 2006. An assessment of climate feedbacks in coupled ocean-atmosphere models. Journal of Climate 19: 3354-3360.

Stephens, G.L. 2005. Cloud feedbacks in the climate system: a critical review. Journal of Climate 18: 237-273.

Stephens B. 2005. Atmospheric moist convection. Annual Review Earth and Planetary Science 33: 605-643.

Vial, J., Dufresne, J.L. and Bony, S. 2013. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dynamics 41:3339-3362.

Webb, M.J., Lambert, F.H. and Gregory, J.M. 2012. Origins of differences in climate sensitivity, forcing and feedback in climate models. Climate Dynamics: 10.1007/s00382-012-1336-x.

Williams, K.D., Ringer, M.A., Senior, C.A., Webb, M.J., McAvaney, B.J., Andronova, N., Bony, S., Dufresne, J.-L.,Emori, S., Gudgel, R., Knutson, T., Li., B., Lo, K., Musat, I., Wegner, J., Slingo, A. and Mitchell, J.F.B. 2006. Evaluation of a component of the cloud response to climate change in an intercomparison of climate models. Climate Dynamics 26: 145-165.

Wyant, M.C., Khairoutdinov, M. and Bretherton, C.S. 2006. Climate sensitivity and cloud response of a GCM with a superparameterization. Geophysical Research Letters 33: 10.1029/2005GL025464.

Zelinka, M.D., Klein, S.A. and Hartmann, D.L. 2012. Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. Journal of Climate 25: 3736-3754.