Abstract
Abstract This paper presents a stochastic triggering parameterization for deep convection and its implementation in the latest standard version of the Laboratoire de Météorologie Dynamique–Zoom (LMDZ) general circulation model: LMDZ5B. The derivation of the formulation of this parameterization and the justification, based on large-eddy simulation results, for the main hypothesis was proposed in Part I of this study. Whereas the standard triggering formulation in LMDZ5B relies on the maximum vertical velocity within a mean bulk thermal, the new formulation presented here (i) considers a thermal size distribution instead of a bulk thermal, (ii) provides a statistical lifting energy at cloud base, (iii) proposes a three-step trigger (appearance of clouds, inhibition crossing, and exceeding of a cross-section threshold), and (iv) includes a stochastic component. Here the complete implementation is presented, with its coupling to the thermal model used to treat shallow convection in LMDZ5B. The parameterization is tested over various cases in a single-column model framework. A sensitivity study to each parameter introduced is also carried out. The impact of the new triggering is then evaluated in the single-column version of LMDZ on several case studies and in full 3D simulations. It is found that the new triggering (i) delays deep convection triggering, (ii) suppresses it over oceanic trade wind cumulus zones, (iii) increases the low-level cloudiness, and (iv) increases the convective variability. The scale-aware nature of this parameterization is also discussed.
Highlights
In the first paper of this series (Rochetin et al 2014, hereafter Part I), a stochastic parameterization of deep convection triggering was formally presented
In GCMs such as LMDZ5B, where shallow and deep convection are represented by separate parameterizations, the triggering scheme is the part of the model that decides whether moist convection should be treated as purely shallow or as mixed shallow and deep
The study of the statistical properties of the thermal spectrum revealed great advantages. It (i) allowed hypotheses to be states for building the thermal distribution parameterization and (ii) suggested the need for a supplementary, stochastic threshold governing the onset of deep convection. This resulted in a new formulation of the transition from shallow to deep convection, which includes a spectral representation of the cloudy thermals and a stochastic triggering of deep convection
Summary
In the first paper of this series (Rochetin et al 2014, hereafter Part I), a stochastic parameterization of deep convection triggering was formally presented. In GCMs such as LMDZ5B, where shallow and deep convection are represented by separate parameterizations, the triggering scheme is the part of the model that decides whether moist convection should be treated as purely shallow or as mixed shallow and deep. It acts at every time step, so the triggering scheme decides when deep convection begins and when it ends.
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