Search Results
You are looking at 1 - 3 of 3 items for :
- Author or Editor: Hui Wan x
- Monthly Weather Review x
- Refine by Access: All Content x
Abstract
The idealized test case proposed by Held and Suarez is carried out with the atmospheric general circulation model ECHAM5 of the Max Planck Institute for Meteorology. The aim is to investigate the sensitivity of the solutions of the spectral dynamical core to spatial and temporal resolution, and to evaluate the numerical convergence of the solutions. Low-frequency fluctuations at time scales as long as thousands of days are found in ultralong integrations. To distinguish the effect of changed resolution from the fluctuations caused by the internal variability, the ensemble method is employed in experiments at resolutions ranging from T31 to T159 with 16 to 81 vertical levels. Significance of the differences between ensembles is assessed by three different statistical tests. Convergence property of the numerical solution is concisely summarized by a ratio index.
Results show that the simulated climate state in the Held–Suarez test is sensitive to spatial resolution. Increase of horizontal resolution leads to slight weakening and poleward shift of the westerly jets. Significant warming is detected in high latitudes, especially near the polar tropopause, while the tropical tropopause becomes cooler. The baroclinic wave activity intensifies considerably with increased horizontal resolution. Higher vertical resolution also leads to stronger eddy variances and cooling near the tropical tropopause, but equatorward shift of the westerly jets. The solutions show an indication of convergence at T85L31 resolution according to all the three statistical tests applied. Differences between integrations with various time steps are judged to be within the noise level induced by the inherent low-frequency variability.
Abstract
The idealized test case proposed by Held and Suarez is carried out with the atmospheric general circulation model ECHAM5 of the Max Planck Institute for Meteorology. The aim is to investigate the sensitivity of the solutions of the spectral dynamical core to spatial and temporal resolution, and to evaluate the numerical convergence of the solutions. Low-frequency fluctuations at time scales as long as thousands of days are found in ultralong integrations. To distinguish the effect of changed resolution from the fluctuations caused by the internal variability, the ensemble method is employed in experiments at resolutions ranging from T31 to T159 with 16 to 81 vertical levels. Significance of the differences between ensembles is assessed by three different statistical tests. Convergence property of the numerical solution is concisely summarized by a ratio index.
Results show that the simulated climate state in the Held–Suarez test is sensitive to spatial resolution. Increase of horizontal resolution leads to slight weakening and poleward shift of the westerly jets. Significant warming is detected in high latitudes, especially near the polar tropopause, while the tropical tropopause becomes cooler. The baroclinic wave activity intensifies considerably with increased horizontal resolution. Higher vertical resolution also leads to stronger eddy variances and cooling near the tropical tropopause, but equatorward shift of the westerly jets. The solutions show an indication of convergence at T85L31 resolution according to all the three statistical tests applied. Differences between integrations with various time steps are judged to be within the noise level induced by the inherent low-frequency variability.
Abstract
Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. In previous studies, it has been demonstrated that a correction term, known in stochastic analysis as the Itô correction, can help improve solution accuracy for various deterministic numerical schemes and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. The formulation is applied to a test problem described by an advection–diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and, hence, helps to reduce the computational cost of the numerical simulation.
Abstract
Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. In previous studies, it has been demonstrated that a correction term, known in stochastic analysis as the Itô correction, can help improve solution accuracy for various deterministic numerical schemes and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. The formulation is applied to a test problem described by an advection–diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and, hence, helps to reduce the computational cost of the numerical simulation.
Abstract
Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.
Abstract
Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.
