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- Author or Editor: Christian Jakob x
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Abstract
The behavior of convection and the Madden–Julian oscillation (MJO) is compared in two simulations from the same global climate model but with two very different treatments of convection: one has a conventional parameterization of moist processes and the other replaces the parameterization with a two-dimensional cloud-resolving model, the so-called superparameterization. The different behavior of local convection and the MJO in the two model simulations reveals that the accurate representation of the following characteristics in the modes of convection might contribute to the improvement of the MJO simulations: (i) precipitation should be an exponentially increasing function of the column saturation fraction, (ii) heavy precipitation should be associated with a stratiform diabatic heating profile, and (iii) there should be a positive relationship between precipitation and surface latent heat flux.
Abstract
The behavior of convection and the Madden–Julian oscillation (MJO) is compared in two simulations from the same global climate model but with two very different treatments of convection: one has a conventional parameterization of moist processes and the other replaces the parameterization with a two-dimensional cloud-resolving model, the so-called superparameterization. The different behavior of local convection and the MJO in the two model simulations reveals that the accurate representation of the following characteristics in the modes of convection might contribute to the improvement of the MJO simulations: (i) precipitation should be an exponentially increasing function of the column saturation fraction, (ii) heavy precipitation should be associated with a stratiform diabatic heating profile, and (iii) there should be a positive relationship between precipitation and surface latent heat flux.
Abstract
Some cumulus clouds with tops between 3 and 7 km (Cu3km–7km) remain in this height region throughout their lifetime (congestus) while others develop into deeper clouds (cumulonimbus). This study describes two techniques to identify the congestus and cumulonimbus cloud types using data from scanning weather radar and identifies the atmospheric conditions that regulate these two modes. A two-wet-season cumulus cloud database of the Darwin C-band polarimetric radar is analyzed and the two modes are identified by examining the 0-dBZ cloud-top height (CTH) of the Cu3km–7km cells over a sequence of radar scans. It is found that ~26% of the classified Cu3km–7km population grow into cumulonimbus clouds. The cumulonimbus cells exhibit reflectivities, rain rates, and drop sizes larger than the congestus cells. The occurrence frequency of cumulonimbus cells peak in the afternoon at ~1500 local time—a few hours after the peak in congestus cells. The analysis of Darwin International Airport radiosonde profiles associated with the two types of cells shows no noticeable difference in the thermal stability rates, but a significant difference in midtropospheric (5–10 km) relative humidity. Moister conditions are found in the hours preceding the cumulonimbus cells when compared with the congestus cells. Using a moisture budget dataset derived for the Darwin region, it is shown that the existence of cumulonimbus cells, and hence deep convection, is mainly determined by the presence of the midtroposphere large-scale upward motion and not merely by the presence of congestus clouds prior to deep convection. This contradicts the thermodynamic viewpoint that the midtroposphere moistening prior to deep convection is solely due to the preceding cumulus congestus cells.
Abstract
Some cumulus clouds with tops between 3 and 7 km (Cu3km–7km) remain in this height region throughout their lifetime (congestus) while others develop into deeper clouds (cumulonimbus). This study describes two techniques to identify the congestus and cumulonimbus cloud types using data from scanning weather radar and identifies the atmospheric conditions that regulate these two modes. A two-wet-season cumulus cloud database of the Darwin C-band polarimetric radar is analyzed and the two modes are identified by examining the 0-dBZ cloud-top height (CTH) of the Cu3km–7km cells over a sequence of radar scans. It is found that ~26% of the classified Cu3km–7km population grow into cumulonimbus clouds. The cumulonimbus cells exhibit reflectivities, rain rates, and drop sizes larger than the congestus cells. The occurrence frequency of cumulonimbus cells peak in the afternoon at ~1500 local time—a few hours after the peak in congestus cells. The analysis of Darwin International Airport radiosonde profiles associated with the two types of cells shows no noticeable difference in the thermal stability rates, but a significant difference in midtropospheric (5–10 km) relative humidity. Moister conditions are found in the hours preceding the cumulonimbus cells when compared with the congestus cells. Using a moisture budget dataset derived for the Darwin region, it is shown that the existence of cumulonimbus cells, and hence deep convection, is mainly determined by the presence of the midtroposphere large-scale upward motion and not merely by the presence of congestus clouds prior to deep convection. This contradicts the thermodynamic viewpoint that the midtroposphere moistening prior to deep convection is solely due to the preceding cumulus congestus cells.
Abstract
The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state—an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
Abstract
The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state—an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
Abstract
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization schemes used in general circulation models.
Abstract
Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization schemes used in general circulation models.
Abstract
Cumulus parameterizations in weather and climate models frequently apply mass-flux schemes in their description of tropical convection. Mass flux constitutes the product of the fractional area covered by convection in a model grid box and the vertical velocity in cumulus clouds. However, vertical velocities are difficult to observe on GCM scales, making the evaluation of mass-flux schemes difficult. Here, the authors combine high-temporal-resolution observations of in-cloud vertical velocities derived from a pair of wind profilers over two wet seasons at Darwin with physical properties of precipitating clouds [cloud-top heights (CTH), convective–stratiform classification] derived from the Darwin C-band polarimetric radar to provide estimates of cumulus mass flux and its constituents. The length of this dataset allows for investigations of the contributions from different cumulus cloud types—namely, congestus, deep, and overshooting convection—to the overall mass flux and of the influence of large-scale conditions on mass flux. The authors found that mass flux was dominated by updrafts and, in particular, the updraft area fraction, with updraft vertical velocity playing a secondary role. The updraft vertical velocities peaked above 10 km where both the updraft area fractions and air densities were small, resulting in a marginal effect on mass-flux values. Downdraft area fractions are much smaller and velocities are much weaker than those in updrafts. The area fraction responded strongly to changes in midlevel large-scale vertical motion and convective inhibition (CIN). In contrast, changes in the lower-tropospheric relative humidity and convective available potential energy (CAPE) strongly modulate in-cloud vertical velocities but have moderate impacts on area fractions. Although average mass flux is found to increase with increasing CTH, it is the environmental conditions that seem to dictate the magnitude of mass flux produced by convection through a combination of effects on area fraction and velocity.
Abstract
Cumulus parameterizations in weather and climate models frequently apply mass-flux schemes in their description of tropical convection. Mass flux constitutes the product of the fractional area covered by convection in a model grid box and the vertical velocity in cumulus clouds. However, vertical velocities are difficult to observe on GCM scales, making the evaluation of mass-flux schemes difficult. Here, the authors combine high-temporal-resolution observations of in-cloud vertical velocities derived from a pair of wind profilers over two wet seasons at Darwin with physical properties of precipitating clouds [cloud-top heights (CTH), convective–stratiform classification] derived from the Darwin C-band polarimetric radar to provide estimates of cumulus mass flux and its constituents. The length of this dataset allows for investigations of the contributions from different cumulus cloud types—namely, congestus, deep, and overshooting convection—to the overall mass flux and of the influence of large-scale conditions on mass flux. The authors found that mass flux was dominated by updrafts and, in particular, the updraft area fraction, with updraft vertical velocity playing a secondary role. The updraft vertical velocities peaked above 10 km where both the updraft area fractions and air densities were small, resulting in a marginal effect on mass-flux values. Downdraft area fractions are much smaller and velocities are much weaker than those in updrafts. The area fraction responded strongly to changes in midlevel large-scale vertical motion and convective inhibition (CIN). In contrast, changes in the lower-tropospheric relative humidity and convective available potential energy (CAPE) strongly modulate in-cloud vertical velocities but have moderate impacts on area fractions. Although average mass flux is found to increase with increasing CTH, it is the environmental conditions that seem to dictate the magnitude of mass flux produced by convection through a combination of effects on area fraction and velocity.
