Search Results
Abstract
Cloud-resolving model simulations of radiative–convective equilibrium are conducted in both two and three dimensions (2D and 3D) to examine the effect of dimensionality on the equilibrium statistics. Convection is forced by a fixed imposed profile of radiative cooling and surface fluxes from fixed temperature ocean.
In the control experiment, using the same number of grid points in both 2D and 3D and a zero mean wind, the temperature and moisture profiles diverge considerably after a few days of simulations. Two mechanisms are shown to be responsible for this. First, 2D geometry causes higher perturbation surface winds resulting from deep convective downdrafts, which lead to a warmer, moister boundary layer and a warmer tropospheric mean temperature state. Additionally, 2D geometry encourages spontaneous large-scale organization, in which areas far away from convection become very dry and thus inhibit further convection there, leading to a drier mean atmosphere.
Further experiments were conducted in which horizontal mean winds were applied, adopting both constant and sheared vertical profiles. With mean surface winds that are of the same magnitude as downdraft outflow velocities or greater, convection can no longer increase mean surface fluxes, and the temperature differences between 2D and 3D are greatly reduced. However, the organization of convection still exists with imposed wind profiles. Repeating the experiments on a small 2D domain produced similar equilibrium profiles to the 3D investigations, since the limited domain artificially reduces surface wind speeds, and also restricts mesoscale organization.
The main conclusions are that for modeling convection that is highly two-dimensionally organized, such as squall lines or Walker-type circulations over strong SST gradients, and for which a reasonable mean surface wind exists, it is possible that a 2D model can be used. However, for random or clustered convection, and especially in low wind environments, it is highly preferable to use a 3D cloud model.
Abstract
Cloud-resolving model simulations of radiative–convective equilibrium are conducted in both two and three dimensions (2D and 3D) to examine the effect of dimensionality on the equilibrium statistics. Convection is forced by a fixed imposed profile of radiative cooling and surface fluxes from fixed temperature ocean.
In the control experiment, using the same number of grid points in both 2D and 3D and a zero mean wind, the temperature and moisture profiles diverge considerably after a few days of simulations. Two mechanisms are shown to be responsible for this. First, 2D geometry causes higher perturbation surface winds resulting from deep convective downdrafts, which lead to a warmer, moister boundary layer and a warmer tropospheric mean temperature state. Additionally, 2D geometry encourages spontaneous large-scale organization, in which areas far away from convection become very dry and thus inhibit further convection there, leading to a drier mean atmosphere.
Further experiments were conducted in which horizontal mean winds were applied, adopting both constant and sheared vertical profiles. With mean surface winds that are of the same magnitude as downdraft outflow velocities or greater, convection can no longer increase mean surface fluxes, and the temperature differences between 2D and 3D are greatly reduced. However, the organization of convection still exists with imposed wind profiles. Repeating the experiments on a small 2D domain produced similar equilibrium profiles to the 3D investigations, since the limited domain artificially reduces surface wind speeds, and also restricts mesoscale organization.
The main conclusions are that for modeling convection that is highly two-dimensionally organized, such as squall lines or Walker-type circulations over strong SST gradients, and for which a reasonable mean surface wind exists, it is possible that a 2D model can be used. However, for random or clustered convection, and especially in low wind environments, it is highly preferable to use a 3D cloud model.
Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
