Quantifying the limits of convective parameterizations

Jones, Todd R.; Randall, David A.

doi:10.1029/2010jd014913

Cited by 56 publications

(62 citation statements)

References 54 publications

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“…These components are sensitive to the spatial and temporal scales of a numerical model. As the horizontal resolution of a model gets higher, the stochastic component of the subgrid-to grid-scale relation becomes more pronounced (Xu et al, 1992;Shutts and Palmer, 2007;Jones and Randall, 2011). At the same time, an increase in horizontal resolution implies a shorter model time step and, as a consequence, a larger impact of the memory component on parameterisation.…”

Section: Introductionmentioning

confidence: 98%

“…In this case, changes in the resolved flow take place on a timescale close to or less than the convective response timescale, and the convective cloud system exhibits a nondiagnostic behaviour (e.g. Pan and Randall, 1998;Jones and Randall, 2011). Along with the effects of time lag in the convective response, memory of convection also comprises a feedback process by which the past interactions between convective elements and thermodynamics fields on the nearcloud scale modify convection at the current time (Davies et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

Sakradzija

Seifert

Heus

2015

Nonlin. Processes Geophys.

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Abstract.We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES) model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds.Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive -the smaller the grid size, the broader the distribution.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

Sakradzija

Seifert

Heus

2015

Nonlin. Processes Geophys.

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“…The goal of cumulus parameterization is to determine changes in the simulated large-scale environment due to the collective influence of multiple cumulus clouds (Jones and Randall, 2011). A statistical approach is used to assume the solution, which always introduces errors, providing an additional source of uncertainty to the stochastic nature of the atmosphere.…”

Section: A F Dos Santos Et Al: Using the Firefly Optimization Methodsmentioning

confidence: 99%

Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

et al. 2013

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Abstract. In this paper we consider an optimization problem applying the metaheuristic Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily precipitation simulations with the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) over South America during January 2006. The method is addressed as a parameter estimation problem to weight the ensemble of precipitation forecasts carried out using different options of the convective parameterization scheme. Ensemble simulations were performed using different choices of closures, representing different formulations of dynamic control (the modulation of convection by the environment) in a deep convection scheme. The optimization problem is solved as an inverse problem of parameter estimation. The application and validation of the methodology is carried out using daily precipitation fields, defined over South America and obtained by merging remote sensing estimations with rain gauge observations. The quadratic difference between the model and observed data was used as the objective function to determine the best combination of the ensemble members to reproduce the observations. To reduce the model rainfall biases, the set of weights determined by the algorithm is used to weight members of an ensemble of model simulations in order to compute a new precipitation field that represents the observed precipitation as closely as possible. The validation of the methodology is carried out using classical statistical scores. The algorithm has produced the best combination of the weights, resulting in a new precipitation field closest to the observations.

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“…Figure 13(a) also show that the spread in CRM ensemble is quite significant compared to the spread in GCM column ensemble. This is expected because CRM solves a set of prognostic equations for its subgrid-scale physics whereas GCM parameterization is purely diagnostic and deterministic (Jones and Randall, 2011).…”

Section: Liquid Water Potential Temperature Budgetmentioning

confidence: 99%

Cloud feedback studies with a physics grid

Dipankar

Stevens

2013

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Preface: This project was funded by DOE in 2007 but work was initially delayed, first because a senior investigator on the original project (Stephen Thomas) took another position, and subsequently because the PI changed institutions, leaving UCLA in the summer of 2008 to join the MPI-M in Hamburg Germany. The project was moved to the MPI-M with the help of DOE staff, and after negotiating numerous logistical and administrative hurdles the project started in April 2010. Due to differences in funding the project funding was able to support roughly three person years, and our institution also agreed to supplement the support to fund the main postdoc for a third year.Due to the change of institutions, and considerable development in dynamical cores since the initiation of the project it was decided to work through the ideas using the new ICON dynamical core. The ICON dynamical core is based on a icosahedral grid and serves a prototype for dynamical cores that are being developed in a number of institutions, including at NCAR (the so called MPAS model) for a future version of the Community Earth System Model. The advantage of using ICON for these purposes is that the the ICON dynamical core is relatively stable, and is built on a hierarchical concept that naturally adapts itself to the proposed experimental strategy. Using ICON also allows the project to benefit from staff at the institute who have helped develop ICON, thus bring the main ideas more rapidly to fruition. However, because of the on-going development in the very core of the model and other issues related to pre and post processing, the project did not end in the expected manner. Although the plans in the proposed project has been achieved, to run the heterogeneous multiscale model at global scale requires very heavy computing resources which was not expected. We have found some encouraging results using the available resources on a smaller domain and we plan to investigate them further on a larger scale in near future.

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Quantifying the limits of convective parameterizations

Cited by 56 publications

References 54 publications

Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

Cloud feedback studies with a physics grid

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