Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics

Monday, 18 April 2016: 4:15 PM
Ponce de Leon A (The Condado Hilton Plaza)
Ming Zhao, NOAA/GFDL, Princeton, NJ; and J. C. Golaz, I. M. Held, V. Ramaswamy, S. J. Lin, Y. Ming, P. Ginoux, B. Wyman, L. Donner, D. Paynter, and H. Guo

Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the inter-model spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, we use a developmental version of the next generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the CMIP5 model ensemble. We demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model's convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere/land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and our current inability to find a clear observational constraint that favors one version of our model over the others, the implications of this ability to engineer climate sensitivity needs to be considered when estimating the uncertainty in climate projections.
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