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- Author or Editor: Catherine Rio x
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Abstract
The “thermal plume model,” a mass-flux scheme combined with a classical diffusive approach, originally developed to represent turbulent transport in the dry convective boundary layer, is extended here to the representation of cloud processes. The modified parameterization is validated in a 1D configuration against results of large eddy simulations (LES), as well as in a 3D configuration against in situ measurements, for a series of cases of dry and cloudy convective boundary layers. Accounting for coherent structures of the mixed layer with the mass-flux scheme improves the representation of the diurnal cycle of the boundary layer, particularly its progressive deepening during the day and the associated near-surface drying. Results also underline the role of the prescription of the mixing of air between the plume and its environment, and of submean-plume fluctuations.
Abstract
The “thermal plume model,” a mass-flux scheme combined with a classical diffusive approach, originally developed to represent turbulent transport in the dry convective boundary layer, is extended here to the representation of cloud processes. The modified parameterization is validated in a 1D configuration against results of large eddy simulations (LES), as well as in a 3D configuration against in situ measurements, for a series of cases of dry and cloudy convective boundary layers. Accounting for coherent structures of the mixed layer with the mass-flux scheme improves the representation of the diurnal cycle of the boundary layer, particularly its progressive deepening during the day and the associated near-surface drying. Results also underline the role of the prescription of the mixing of air between the plume and its environment, and of submean-plume fluctuations.
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.
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.
Abstract
This paper proposes a new formulation of the deep convection triggering for general circulation model convective parameterizations. This triggering is driven by evolving properties of the strongest boundary layer thermals. To investigate this, a statistical analysis of large-eddy simulation cloud fields in a case of transition from shallow to deep convection over a semiarid land is carried out at different stages of the transition from shallow to deep convection. Based on the dynamical and geometrical properties at cloud base, a new computation of the triggering is first proposed. The analysis of the distribution law of the maximum size of the thermals suggests that, in addition to this necessary condition, another triggering condition is required, that is, that this maximum horizontal size should exceed a certain threshold. This is explicitly represented stochastically. Therefore, the new formulation integrates the whole transition process from the first cloud to the first deep convective cell and can be decomposed into three steps: (i) the appearance of clouds, (ii) crossing of the inhibition layer, and (iii) deep convection triggering.
Abstract
This paper proposes a new formulation of the deep convection triggering for general circulation model convective parameterizations. This triggering is driven by evolving properties of the strongest boundary layer thermals. To investigate this, a statistical analysis of large-eddy simulation cloud fields in a case of transition from shallow to deep convection over a semiarid land is carried out at different stages of the transition from shallow to deep convection. Based on the dynamical and geometrical properties at cloud base, a new computation of the triggering is first proposed. The analysis of the distribution law of the maximum size of the thermals suggests that, in addition to this necessary condition, another triggering condition is required, that is, that this maximum horizontal size should exceed a certain threshold. This is explicitly represented stochastically. Therefore, the new formulation integrates the whole transition process from the first cloud to the first deep convective cell and can be decomposed into three steps: (i) the appearance of clouds, (ii) crossing of the inhibition layer, and (iii) deep convection triggering.
Abstract
On 10 July 2006, during the Special Observation Period (SOP) of the African Monsoon Multidisciplinary Analysis (AMMA) campaign, a small convective system initiated over Niamey and propagated westward in the vicinity of several instruments activated in the area, including the Massachusetts Institute of Technology (MIT) C-band Doppler radar and the Atmospheric Radiation Measurement (ARM) mobile facility. The system started after a typical convective development of the planetary boundary layer. It grew and propagated within the scope of the radar range, so that its entire life cycle is documented, from the precluding shallow convection to its traveling gust front. The analysis of the observations during the transitions from organized dry convection to shallow convection and from shallow convection to deep convection lends support to the significant role played by surface temperature heterogeneities and boundary layer processes in the initiation of deep convection in semiarid conditions. The analysis of the system later in the day, of its growth and propagation, and of its associated density current allows the authors to estimate the wake available potential energy and demonstrate its capability to trigger deep convection itself. Given the quality and density of observations related to this case, and its typical and quasi-textbook characteristics, this is considered a prime case for the study of initiation and evolution of deep convection, and for testing their parameterizations in single-column models.
Abstract
On 10 July 2006, during the Special Observation Period (SOP) of the African Monsoon Multidisciplinary Analysis (AMMA) campaign, a small convective system initiated over Niamey and propagated westward in the vicinity of several instruments activated in the area, including the Massachusetts Institute of Technology (MIT) C-band Doppler radar and the Atmospheric Radiation Measurement (ARM) mobile facility. The system started after a typical convective development of the planetary boundary layer. It grew and propagated within the scope of the radar range, so that its entire life cycle is documented, from the precluding shallow convection to its traveling gust front. The analysis of the observations during the transitions from organized dry convection to shallow convection and from shallow convection to deep convection lends support to the significant role played by surface temperature heterogeneities and boundary layer processes in the initiation of deep convection in semiarid conditions. The analysis of the system later in the day, of its growth and propagation, and of its associated density current allows the authors to estimate the wake available potential energy and demonstrate its capability to trigger deep convection itself. Given the quality and density of observations related to this case, and its typical and quasi-textbook characteristics, this is considered a prime case for the study of initiation and evolution of deep convection, and for testing their parameterizations in single-column models.
Abstract
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
Abstract
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
