This data set contains the simulation outputs used in the study summarized below:

Systematic biases in the representation of boundary layer (BL) clouds are a leading source of

uncertainty in climate projections. A variation on superparameterization (SP) called ‘‘ultraparameterization’’

(UP) is developed, in which the grid spacing of the cloud-resolving models (CRMs) is fine enough

(250x20 m) to explicitly capture the BL turbulence, associated clouds, and entrainment in a global climate

model capable of multiyear simulations. UP is implemented within the Community Atmosphere Model

using 2-degree resolution (14,000 embedded CRMs) with one-moment microphysics. By using a small domain

and mean-state acceleration, UP is computationally feasible today and promising for exascale computers.

Short-duration global UP hindcasts are compared with SP and satellite observations of top-of-atmosphere

radiation and cloud vertical structure. The most encouraging improvement is a deeper BL and more realistic

vertical structure of subtropical stratocumulus (Sc) clouds, due to stronger vertical eddy motions that

promote entrainment. Results from 90 day integrations show climatological errors that are competitive with

SP, with a significant improvement in the diurnal cycle of offshore Sc liquid water. Ongoing concerns with

the current UP implementation include a dim bias for near-coastal Sc that also occurs less prominently in SP

and a bright bias over tropical continental deep convection zones. Nevertheless, UP makes global

eddy-permitting simulation a feasible and interesting alternative to conventionally parameterized GCMs or

SP-GCMs with turbulence parameterizations for studying BL cloud-climate and cloud-aerosol feedback.

All instructions and information are provided in the attached Readme.txt file. Please download the latest version for the full data set.