16.4 Cloud Response to Surface Warming in Global Ultra-Parameterizaiton with Explicit Embedded Boundary Layer Turbulence and Shallow Clouds

Friday, 13 July 2018: 2:15 PM
Regency D (Hyatt Regency Vancouver)
Hossein Parishani, Univ. of California, Irvine, CA; and M. S. Pritchard, C. R. Terai, C. S. Bretherton, M. Wyant, and M. Khairoutdinov

Cloud feedback represents one of the most uncertain aspects of the climate system in current climate models, due to biases and parameterization formulation uncertainties. Many of the differences in cloud feedback across climate models stem from differences in representations of boundary layer processes in model parameterizations. We study the cloud response under a +4K surface warming scenario in a recently developed, low-cloud permitting climate model with explicit boundary layer turbulence (i.e. ``Ultra-Parameterized (UP)’’ Community Atmosphere Model, UPCAM), and compare its predictions against those from a standard Super-Parameterization (SP) model. In a recent publication we have shown that UP, while not without its own complexity trade-offs, can produce encouraging improvements in low cloud climatology in multi-month simulations of the present climate and is a promising target for exascale computing (Parishani et al. 2017).

UP’s microphysics is tuned to best match the model present-day, top-of-atmosphere radiation fluxes with satellite observations. In this tuned framework, UP significantly reduces the high-latitude low cloud fraction, as compared to SP. In the warmed climate, UP also dampens SP’s large increase in liquid cloud water and associated negative cloud feedback in high latitudes. SP shows an almost net zero cloud response, while UP's damped high latitude clouds lead to a slightly positive net cloud feedback, though insignificant considering the uncertainty bounds. As with SP, low-cloud feedbacks are positive over land, negative at high latitudes, and weak over the low-latitude oceans. The implied global cloud feedback is positive but within the lower half of the range of CMIP5 models.


Parishani, H., M. S. Pritchard, C. S. Bretherton, M. C. Wyant, and M. Khairoutdinov (2017), Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence, J. Adv. Model. Earth Syst., 9, doi:10.1002/2017MS000968.

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