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Low Cloud Feedback to Surface Warming in the World's First Global Climate Model with Explicit Embedded Boundary Layer Turbulence

Presentation Date
Thursday, December 14, 2017 at 1:40pm
New Orleans Ernest N. Morial Convention Center - Poster Hall D-F



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 results of cloud feedback to +4K SST warming in a new experimental climate model, the ``Ultra-Parameterized (UP)’’ Community Atmosphere Model, UPCAM. We have developed UPCAM as an unusually high-resolution implementation of cloud superparameterization (SP) in which a global set of cloud resolving arrays is embedded in a host global climate model. In UP, the cloud-resolving scale includes sufficient internal resolution 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 scales. 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). Here we show results of its low cloud feedback to warming in multi-year simulations for the first time.