Low Cloud Feedback in an Ultraparameterized Global Climate Model with Explicitly Simulated Boundary Layer Turbulence

Biases and parameterization formulation uncertainties in the representation of boundary layer clouds remain a leading source of possible systematic error in climate projections. Here we show the first multi-year simulations of cloud feedback to +4K SST warming with a new experimental climate model, the ``Ultraparameterized (UP)’’ Community Atmosphere Model, UPCAM. We have developed UPCAM as a high-resolution implementation of superparameterization (SP) in which a global set of cloud resolving models (CRMs) is embedded in a host global climate model. In UP, the CRM has 250 m horizontal and 20 m vertical resolution in the lower troposphere to explicitly generate the turbulent eddies that form marine stratocumulus and trade cumulus clouds. This is computationally costly but complements other available approaches for studying low clouds and their climate interaction by avoiding parameterization of the relevant cloud-forming scales. UP is found to produce credible simulations of boundary-layer cloud processes in the current climate comparable to SP (Parishani et al. 2017). 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.

Reference:

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.