Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence -- simulation data

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.