Description

The scale-aware mass-flux (SAMF) deep convection scheme is an updated version of the previous Simplified Arakawa-Schubert (SAS) scheme with scale and aerosol awareness and parameterizes the effect of deep convection on the environment (represented by the model state variables) in the following way. First, a simple cloud model is used to determine the change in model state variables due to one entraining/detraining cloud type, per unit cloud-base mass flux. Next, the total change in state variables is retrieved by determining the actual cloud base mass flux using the quasi-equilibrium assumption (for grid sizes larger than a threshold value [currently set to 8 km]) or a mean updraft velocity (for grid sizes smaller than the threshold value). With a scale-aware parameterization, the cloud mass flux decreases with increasing grid resolution. A simple aerosol-aware parameterization is employed, where rain conversion in the convective updraft is modified by aerosol number concentration. The name SAS is replaced with SAMF as for the smaller grid sizes, the parameterization does not use Arakawa-Schubert's quasi-equilibrium assumption any longer where the cloud work function (interpreted as entrainment-moderated convective available potential energy [CAPE]) by the large scale dynamics is in balance with the consumption of the cloud work function by the convection.

The SAS scheme uses the working concepts put forth in Arakawa and Schubert (1974) [6] but includes modifications and simplifications from Grell (1993) [73] such as saturated downdrafts and only one cloud type (the deepest possible), rather than a spectrum based on cloud top heights or assumed entrainment rates. The scheme was implemented for the GFS in 1995 by Pan and Wu (1995) [159], with further modifications discussed in Han and Pan (2011) [78], including the calculation of cloud top, a greater CFL-criterion-based maximum cloud base mass flux, updated cloud model entrainment and detrainment, improved convective transport of horizontal momentum, a more general triggering function, and the inclusion of convective overshooting.

The SAMF scheme updates the SAS scheme with scale- and aerosol-aware parameterizations from Han et al. (2017) [80] based on the studies by Arakawa and Wu (2013) [7] and Grell and Freitas (2014) [72] for scale awareness and Lim (2011) by [118] for aerosol awareness. The ratio of advective time to convective turnover time is also taken into account for the scale-aware parameterization. Along with the scale- and aerosol-aware parameterizations, more changes are made to the SAMF scheme. The cloud base mass-flux computation is modified to use convective turnover time as the convective adjustment time scale. The rain conversion rate is modified to decrease with decreasing air temperature above the freezing level. Convective inhibition in the sub-cloud layer is used as an additional trigger condition. Convective cloudiness is enhanced by considering suspended cloud condensate in the updraft. The lateral entrainment is also enhanced to more strongly suppress convection in a drier environment.

GFSv16 Updates

In further update for FY19 GFSv16 implementation, interaction with turbulent kinetic energy (TKE), which is a prognostic variable used in a scale-aware TKE-based moist EDMF vertical turbulent mixing scheme, is included. Entrainment rates in updrafts and downdrafts are proportional to sub-cloud mean TKE. TKE is transported by cumulus convection. TKE contribution from cumulus convection is deduced from cumulus mass flux. On the other hand, tracers such as ozone and aerosol are also transported by cumulus convection.

Occasional model crashes occurred when stochastic physics is on, due to too strong convective cooling and heating tendencies near the cumulus top which are amplified by stochastic physics. In order to alleviate this, the convection schemes were modified for the rain conversion rate, entrainment and detrainment rates, overshooting layers, and maximum allowable cloudbase mass flux (as of June 2018).

GFS saSAS Scheme Updates in GFSv17

The updates to the SAMF parameterization described above, between GFSv16 and GFSv17 are carefully outlined in Bengtsson and Han (2004)(submitted to WAF). The main updates include:

Implementation of a positive definition mass-flux scheme and a method for removing the negative tracers (Han et al. 2022 [82])
Introduction of a new closure based on a prognostic evolution of the convective updraft area fraction in both shallow and deep convection (Bengtsson et al. 2022 [16])
Introduction of 3D effects of cold-pool dynamics and stochastic initiation using self-organizing cellular_automata (Bengtsson et al. 2021 [15])
Introduction of environmental wind shear and TKE dependence in convection, to seek improvements in hurricane forecast prediction (Han et al. 2024 [83])
Stricter convective initiation criteria to allow for more CAPE to build up to address a low CAPE bias in GFSv16 (Han et al. 2021 [81])
Reduction of convective rain evaporation rate to address a systematic cold bias near the surface in GFSv16 (Han et al. 2021 [81])

See also: NCEP Office Note 505 [84] and 506 [https://doi.org/10.25923/5051-3r70]

Intraphysics Communication

Argument Table

General Algorithm

GFS samfdeepcnv General Algorithm