Abstract
In this study, the spatial structure of cumulus cloud populations is investigated using three-dimensional snapshots from large-domain LES experiments. The aim is to understand and quantify ...the internal variability in cloud size distributions due to subsampling effects and spatial organization. A set of idealized shallow cumulus cases is selected with varying degrees of spatial organization, including a slowly organizing marine precipitating case and five more quickly organizing diurnal cases over land. A subdomain analysis is applied, yielding cloud number distributions at sample sizes ranging from severely undersampled to nearly complete. A strong power-law scaling is found in the relation between cloud number variability and subdomain size, reflecting an inverse linear relation. Scaling subdomain size by cloud size yields a data collapse across time points and cases, highlighting the role played by cloud spacing in controlling the stochastic variability. Spatial organization acts on top of this baseline model by increasing the maximum cloud size and by enhancing the variability in the number of smallest clouds. This reflects that the smaller clouds start to live on top of larger-scale thermodynamic structures, such as cold pools, which favor or inhibit their formation. Compositing all continental cumulus cases suggests the existence of a prototype diurnal time dependence in the spatial organization. A simple stochastic expression for cloud number variability is proposed that is formulated in terms of two dimensionless groups, which allows objective estimation of the degree of spatial organization in simulated and observed cumulus cloud populations.
This study explores a mass flux framework for moist convective transport and clouds that is formulated in terms of discretized size densities. The properties of each bin in these histograms are ...estimated individually, making use of a rising plume model. In this framework, the number density acts as a weight, appearing in the area fraction of the mass flux. Such “bin‐macrophysics” models have the benefit that bulk closures become redundant, and that scale‐awareness is introduced at the basis of the formulation. Large‐eddy simulation results are used to verify the design of this framework and to constrain associated constants of proportionality. The behavior of the framework is explored by means of single‐column model simulations of various idealized cases of shallow and deeper surface‐driven convection. A smoothly developing solution for a deepening marine shallow cumulus case is obtained, reproducing key aspects of transport and clouds that define this regime. Further investigation of the size statistics of the framework reveals that indirect interactions between size‐bins play a key role in the equilibration process. An “acceleration‐detrainment” layer is identified above cloud base in which the flux uptake by the largest plumes is counteracted by the detrainment by decelerating smaller plumes. This suppresses CIN, and thus acts to preserve the cloud‐subcloud coupling. The convective mass flux shows sensitivity to environmental humidity in the deeper convective cases, reproducing transitions from shallow‐to‐deep convection. Sensitivity tests are performed to assess the impact of various components of the framework.
Key Points:
A scale‐aware mass flux scheme based on resolved size densities is explored
Interactions between sizes play a key role in boundary layer equilibration
Humidity‐convection feedbacks and shallow‐to‐deep transitions are reproduced
Uncertainties in numerical predictions of weather and climate are often linked to the representation of unresolved processes that act relatively quickly compared to the resolved general circulation. ...These processes include turbulence, convection, clouds, and radiation. Single-column model (SCM) simulation of idealized cases and the subsequent evaluation against large-eddy simulation (LES) results has become an often used and relied on method to obtain insight at process level into the behavior of such parameterization schemes; benefits of SCM simulation are the enhanced model transparency and the high computational efficiency. Although this approach has achieved demonstrable success, some shortcomings have been identified; among these, i) the statistical significance and relevance of single idealized case studies might be questioned and ii) the use of observational datasets has been relatively limited. A recently initiated project named the Royal Netherlands Meteorological Institute (KNMI) Parameterization Testbed (KPT) is part of a general move toward a more statistically significant process-level evaluation, with the purpose of optimizing the identification of problems in general circulation models that are related to parameterization schemes. The main strategy of KPT is to apply continuous long-term SCM simulation and LES at various permanent meteorological sites, in combination with comprehensive evaluation against observations at multiple time scales. We argue that this strategy enables the reproduction of typical long-term mean behavior of fast physics in large-scale models, but it still preserves the benefits of single-case studies (such as model transparency). This facilitates the tracing and understanding of errors in parameterization schemes, which should eventually lead to a reduction of related uncertainties in numerical predictions of weather and climate.
This study explores the opportunities created by subjecting a system of interacting fast-acting parameterizations to long-term single-column model evaluation against multiple independent measurements ...at a permanent meteorological site. It is argued that constraining the system at multiple key points facilitates the tracing and identification of compensating errors between individual parametric components. The extended time range of the evaluation helps to enhance the statistical significance and representativeness of the single-column model result, which facilitates the attribution of model behavior as diagnosed in a general circulation model to its subgrid parameterizations. At the same time, the high model transparency and computational efficiency typical of single-column modeling is preserved.
The method is illustrated by investigating the impact of a model change in the Regional Atmospheric Climate Model (RACMO) on the representation of the coupled boundary layer–soil system at the Cabauw meteorological site in the Netherlands. A set of 12 relevant variables is defined that covers all involved processes, including cloud structure and amplitude, radiative transfer, the surface energy budget, and the thermodynamic state of the soil and various heights of the lower atmosphere. These variables are either routinely measured at the Cabauw site or are obtained from continuous large-eddy simulation at that site. This 12-point check proves effective in revealing the existence of a compensating error between cloud structure and radiative transfer, residing in the cloud overlap assumption. In this exercise, the application of conditional sampling proves a valuable tool in establishing which cloud regime exhibits the biggest impact.
In this study, a spectral model for convective transport is coupled to a thermal population model on a two‐dimensional horizontal “microgrid,” covering the typical gridbox size of general circulation ...models. The goal is to explore new ways of representing impacts of spatial organization in cumulus cloud fields. The thermals are considered the smallest building block of convection, with thermal life cycle and movement represented through binomial functions. Thermals interact through two simple rules, reflecting pulsating growth and environmental deformation. Long‐lived thermal clusters thus form on the microgrid, exhibiting scale growth and spacing that represent simple forms of spatial organization and memory. Size distributions of cluster number are diagnosed from the microgrid through an online clustering algorithm, and provided as input to a spectral multiplume eddy‐diffusivity mass flux scheme. This yields a decentralized transport system, in that the thermal clusters acting as independent but interacting nodes that carry information about spatial structure. The main objectives of this study are (a) to seek proof of concept of this approach, and (b) to gain insight into impacts of spatial organization on convective transport. Single‐column model experiments demonstrate satisfactory skill in reproducing two observed cases of continental shallow convection. Metrics expressing self‐organization and spatial organization match well with large‐eddy simulation results. We find that in this coupled system, spatial organization impacts convective transport primarily through the scale break in the size distribution of cluster number. The rooting of saturated plumes in the subcloud mixed layer plays a key role in this process.
Plain Language Summary
Recent studies have emphasized the importance of the spatial structure of convective cloud fields in Earth's climate, yet this phenomenon is not yet represented well in Earth System Models (ESMs). This study explores a new way to achieve this goal, by considering spatial organization at the scale of small bubbles of rising air called thermals that together make up convective clouds. Populations of interacting thermals are modeled in a computationally efficient way on a small two‐dimensional grid. This microgrid is then coupled to a convection scheme, which stands for the set of equations used to statistically represent the impact of convective transport at scales that remain unresolved in ESMs. The coupling makes the scheme decentralized, in that the transport becomes dependent on a population of longer‐lived convective structures that slowly develop and evolve on the microgrid. The new scheme is tested for observed conditions at a meteorological site in the Southern Great Plains area of the United States, making use of a combination of high‐resolution simulations and measurements to evaluate performance. Apart from proof of concept for the new modeling approach, the results provide new insights into how the spatial structure of convective cloud populations can affect its vertical transport.
Key Points
A multiplume spectral convection scheme is coupled to a binomial thermal population model on a horizontal microgrid
Observed diurnal cycles of continental shallow convection are reproduced, including good agreement on scale growth and spatial organization
Spatial organization impacts convective transport through the scale break in the cluster number density, with a key role played by plume rooting
This study explores ways of establishing the characteristic behavior of boundary layer schemes in representing subtropical marine low‐level clouds in climate models. To this purpose, parameterization ...schemes are studied in both isolated and interactive mode with the larger‐scale circulation. Results of the EUCLIPSE/GASS intercomparison study for Single‐Column Models (SCM) on low‐level cloud transitions are compared to General Circulation Model (GCM) results from the CFMIP‐2 project at selected grid points in the subtropical eastern Pacific. Low cloud characteristics are plotted as a function of key state variables for which Large‐Eddy Simulation results suggest a distinct and reasonably tight relation. These include the Cloud Top Entrainment Instability (CTEI) parameter and the total cloud cover. SCM and GCM results are thus compared and their resemblance is quantified using simple metrics. Good agreement is reported, to such a degree that SCM results are found to be uniquely representative of their GCM, and vice versa. This suggests that the system of parameterized fast boundary layer physics dominates the model state at any given time, even when interactive with the larger‐scale flow. This behavior can loosely be interpreted as a unique “fingerprint” of a boundary layer scheme, recognizable in both SCM and GCM simulations. The result justifies and advocates the use of SCM simulation for improving weather and climate models, including the attribution of typical responses of low clouds to climate change in a GCM to specific parameterizations.
Key Points:
The behavior of low‐level clouds in climate models is attributed to parameterizations
The resemblance between SCM and GCM results is assessed and quantified
Boundary layer schemes are found to have a unique, characteristic behavior
In this study Lagrangian large‐eddy simulation of cloudy mixed layers in evolving warm air masses in the Arctic is constrained by in situ observations from the recent PASCAL field campaign. A key ...novelty is that time dependence is maintained in the large‐scale forcings. An iterative procedure featuring large‐eddy simulation on microgrids is explored to calibrate the case setup, inspired by and making use of the typically long memory of Arctic air masses for upstream conditions. The simulated mixed‐phase clouds are part of a turbulent mixed layer that is weakly coupled to the surface and is occasionally capped by a shallow humidity layer. All eight simulated mixed layers exhibit a strong time evolution across a range of time scales, including diurnal but also synoptic fingerprints. A few cases experience rapid cloud collapse, coinciding with a rapid decrease in mixed‐layer depth. To gain insight, composite budget analyses are performed. In the mixed‐layer interior the heat and moisture budgets are dominated by turbulent transport, radiative cooling, and precipitation. However, near the thermal inversion the large‐scale vertical advection also contributes significantly, showing a distinct difference between subsidence and upsidence conditions. A bulk mass budget analysis reveals that entrainment deepening behaves almost time‐constantly, as long as clouds are present. In contrast, large‐scale subsidence fluctuates much more strongly and can both counteract and boost boundary‐layer deepening resulting from entrainment. Strong and sudden subsidence events following prolonged deepening periods are found to cause the cloud collapses, associated with a substantial reduction in the surface downward longwave radiative flux.
Key Points
Lagrangian LES of Arctic cloudy mixed layers in evolving warm air masses is constrained by in situ observations from the PASCAL field campaign
A novel iterative method relying on LES on microgrids is applied to optimize the case configuration and adjust biases in GCM‐derived forcings
Budget studies give insight into local and remote controls on AML evolution, suggesting large‐scale subsidence events can cause low‐level cloud collapse over the sea ice
This study explores how to drive Single‐Column Models (SCMs) with existing data sets of General Circulation Model (GCM) outputs, with the aim of studying the boundary layer cloud response to climate ...change in the marine subtropical trade wind regime. The EC‐EARTH SCM is driven with the large‐scale tendencies and boundary conditions as derived from two different data sets, consisting of high‐frequency outputs of GCM simulations. SCM simulations are performed near Barbados Cloud Observatory in the dry season (January–April), when fair‐weather cumulus is the dominant low‐cloud regime. This climate regime is characterized by a near equilibrium in the free troposphere between the long‐wave radiative cooling and the large‐scale advection of warm air. In the SCM, this equilibrium is ensured by scaling the monthly mean dynamical tendency of temperature and humidity such that it balances that of the model physics in the free troposphere. In this setup, the high‐frequency variability in the forcing is maintained, and the boundary layer physics acts freely. This technique yields representative cloud amount and structure in the SCM for the current climate. Furthermore, the cloud response to a sea surface warming of 4 K as produced by the SCM is consistent with that of the forcing GCM.
Plain Language Summary
The cloud response to climate change remains one of the main uncertainties in the predictions of climate models. Shallow‐cumulus clouds have been identified as the cloud regime that contributes the most to the inter‐model spread. This is due to both their key importance in the climate system and to their persistent occurrence all over the globe. To move forward, it is indeed necessary to gain insight into the reasons of the inter‐model differences. This article presents a new method aimed to tackle this problem. The analysis explores the advantages and the applicability of this novel technique. The results prove that such a method is an effective way to exploit existing data sets for testing climate models and for increasing our understanding in the shallow‐cumulus clouds response.
Key Points
High‐frequency output of GCMs is used as prescribed large‐scale forcings in long‐term SCM simulations of the Caribbean dry season
The free troposphere is kept in approximate radiative‐advective equilibrium, while the fast boundary layer physics are free to act
The low‐level clouds of the GCM in current climate and their response to a climate perturbation are both reproduced with this method
A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, ...across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ—the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June–July–August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions as compared to the first International Satellite Cloud Climatology Project (ISCCP) is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.
High‐resolution ground‐based measurements are used to assess the realism of fine‐scale numerical simulations of shallow cumulus cloud fields. The overlap statistics of cumuli as produced by ...large‐eddy simulations (LES) are confronted with Cloudnet data sets at the Jülich Observatory for Cloud Evolution. The Cloudnet pixel is small enough to detect cumuliform cloud overlap. Cloud fraction masks are derived for five different cases, using gridded time‐height data sets at various temporal and vertical resolutions. The overlap ratio (R), i.e., the ratio between cloud fraction by volume and by area, is studied as a function of the vertical resolution. Good agreement is found between R derived from observations and simulations. An inverse linear function is found to best describe the observed overlap behavior, confirming previous LES results. Simulated and observed decorrelation lengths are smaller (∼300 m) than previously reported (>1 km). A similar diurnal variation in the overlap efficiency is found in observations and simulations.
Key Points
Inefficient cloud overlap at small scales is now supported by observations
Inverse linear functions best describe cloud overlap
LES and observations both indicate a diurnal cycle in the overlap parameter
PDF
