Search Results
You are looking at 1 - 10 of 19 items for
- Author or Editor: A. Navarra x
- Refine by Access: All Content x
Abstract
An anomaly model linearized around the observed winter climatology is used to study the steady response of the atmosphere to diabatic heating. The model is an R7, nine vertical levels, primitive equations, fully spectral model, derived from the GFDL GCM (Geophysical Fluid Dynamics Laboratory's General Circulation Model). The anomaly model is capable of treating basic states that depend on latitude, longitude and height. The Krylov technique is used to solve the linear equations. This generality allows the treatment of the important problem of linear waves in the atmosphere from a more general paint of view; a larger class (zonally asymmetric) of basic states can now be treated for the baroclinic primitive equations. The (R7) linear anomaly model is used to investigate the linear response to equatorial and midlatitude prescribed heating. The results indicate that the solution is affected by the presence of the stationary waves in the basic state. In particular, in the case of midlatitude heating large responses can be obtained for some locations of the heating. However, because of the low resolution used in these experiments no firm conclusion can be drawn on the role of baroclinic effects.
The most sensitive areas are identified in some preliminary sensitivity experiments. In the equatorial heating case they correspond to equatorial heating positioned south of the main jet stream. In the midlatitude heating case, a large response is obtained with shallow heating placed at the beginning of the Asian jet stream.
Abstract
An anomaly model linearized around the observed winter climatology is used to study the steady response of the atmosphere to diabatic heating. The model is an R7, nine vertical levels, primitive equations, fully spectral model, derived from the GFDL GCM (Geophysical Fluid Dynamics Laboratory's General Circulation Model). The anomaly model is capable of treating basic states that depend on latitude, longitude and height. The Krylov technique is used to solve the linear equations. This generality allows the treatment of the important problem of linear waves in the atmosphere from a more general paint of view; a larger class (zonally asymmetric) of basic states can now be treated for the baroclinic primitive equations. The (R7) linear anomaly model is used to investigate the linear response to equatorial and midlatitude prescribed heating. The results indicate that the solution is affected by the presence of the stationary waves in the basic state. In particular, in the case of midlatitude heating large responses can be obtained for some locations of the heating. However, because of the low resolution used in these experiments no firm conclusion can be drawn on the role of baroclinic effects.
The most sensitive areas are identified in some preliminary sensitivity experiments. In the equatorial heating case they correspond to equatorial heating positioned south of the main jet stream. In the midlatitude heating case, a large response is obtained with shallow heating placed at the beginning of the Asian jet stream.
Abstract
The Schmidt decomposition is applied to the evolution operator of the linearized barotropic equation on a sphere (in the following referred to as the barotropic propagator) to study the evolution of the variance, that is, of the collective evolution of a cloud of trajectories centered around the initial condition. The variance can give reliable information on the tendency that some initial conditions may have to generate large spreads in the subsequent time evolution, especially when many modes with similarly large amplifying rates exist. It appears rather arbitrary, under these circumstances, to pick a particular mode just because it happens to have the largest rate for that particular numerical formulation and resolution setting. It is also shown that the Golden-Thompson generalized inequality and other indicators can be used to estimate the linear variance from the analysis of the initial condition itself, without the need for performing the costly explicit calculation of the propagator.
Numerical experiments performed on a set of initial conditions obtained from a simulation experiment and from observations show that in a barotropic model a spread index based on an indicator of non-self-adjointness, as the Golden-Thompson index, is capable of detecting with good reliability initial conditions with a tendency to produce large spreads.
Abstract
The Schmidt decomposition is applied to the evolution operator of the linearized barotropic equation on a sphere (in the following referred to as the barotropic propagator) to study the evolution of the variance, that is, of the collective evolution of a cloud of trajectories centered around the initial condition. The variance can give reliable information on the tendency that some initial conditions may have to generate large spreads in the subsequent time evolution, especially when many modes with similarly large amplifying rates exist. It appears rather arbitrary, under these circumstances, to pick a particular mode just because it happens to have the largest rate for that particular numerical formulation and resolution setting. It is also shown that the Golden-Thompson generalized inequality and other indicators can be used to estimate the linear variance from the analysis of the initial condition itself, without the need for performing the costly explicit calculation of the propagator.
Numerical experiments performed on a set of initial conditions obtained from a simulation experiment and from observations show that in a barotropic model a spread index based on an indicator of non-self-adjointness, as the Golden-Thompson index, is capable of detecting with good reliability initial conditions with a tendency to produce large spreads.
Abstract
Anomally models based on a spectral general circulation model (GCM) are formulated and applied to study of low-frequency atmospheric variability in the extratropics, and long-range forecasting research. A steady linear version of the anomaly model is treated by a matrix method. This model consists of nine vertical levels, 15 wave rhomboidal truncation, primitive equation system, and a fixed basic state, which is three-dimensionally variable. The matrix to be handled is extremely large, but can be solved using Krylov's technique. The solutions represent various teleconnection patterns known in the observed atmosphere. The sensitivity of the response of this anomaly model to zonally variability of the temporally fixed basic fields and to the geographical position of tropical heatings is investigated. The solutions of the steady linear anomaly model are compared with those of the original GCM, revealing that there are a few similarities among the solutions, but considerable discrepancies are also evident. A time-dependent nonlinear anomaly model is applied to further investigate the discrepancy. It appears that transient are crucial for explaining the disagreement, although the study with the time-dependent anomaly model is preliminary.
A noteworthy aspect of the overall approach is that the anomaly models are derived, with only small modifications, from the full GCM, and therefore, their relationship can be readily investigated. It is concluded that the steady linear model may be used as a diagnostic tool for investigating the characteristics of the full GCM and the dynamics of a particular state of the atmosphere. However, caution is needed when there is a significant role played by transient eddies, and in the treatment of tropical Rayleigh friction.
Abstract
Anomally models based on a spectral general circulation model (GCM) are formulated and applied to study of low-frequency atmospheric variability in the extratropics, and long-range forecasting research. A steady linear version of the anomaly model is treated by a matrix method. This model consists of nine vertical levels, 15 wave rhomboidal truncation, primitive equation system, and a fixed basic state, which is three-dimensionally variable. The matrix to be handled is extremely large, but can be solved using Krylov's technique. The solutions represent various teleconnection patterns known in the observed atmosphere. The sensitivity of the response of this anomaly model to zonally variability of the temporally fixed basic fields and to the geographical position of tropical heatings is investigated. The solutions of the steady linear anomaly model are compared with those of the original GCM, revealing that there are a few similarities among the solutions, but considerable discrepancies are also evident. A time-dependent nonlinear anomaly model is applied to further investigate the discrepancy. It appears that transient are crucial for explaining the disagreement, although the study with the time-dependent anomaly model is preliminary.
A noteworthy aspect of the overall approach is that the anomaly models are derived, with only small modifications, from the full GCM, and therefore, their relationship can be readily investigated. It is concluded that the steady linear model may be used as a diagnostic tool for investigating the characteristics of the full GCM and the dynamics of a particular state of the atmosphere. However, caution is needed when there is a significant role played by transient eddies, and in the treatment of tropical Rayleigh friction.
Abstract
A new method is presented to detect the portion of variability connected between two climatic fields. The method is a realization of the Procrustes problem, and it is a generalization of methods for analysis of variance such as the singular value decomposition (SVD) or canonical correlation analysis (CCA). The Procrustes formulation offers a general framework to link together variance analysis methods, and regression methods, including as special cases SVD and CCA.
Using this approach two fields can be divided into a subspace where variations of one field are connected to variations of the other field, the coupled manifold, and a subspace where variations are independent, the free manifold. The unified approach can be applied to prescribed SST experiments, in which case it recovers consistent results with other methods designed to identify the forced portion of variance, but it can now be extended also to the coupled case or to observations.
Some examples from prescribed SST simulation experiments and observations are discussed.
Abstract
A new method is presented to detect the portion of variability connected between two climatic fields. The method is a realization of the Procrustes problem, and it is a generalization of methods for analysis of variance such as the singular value decomposition (SVD) or canonical correlation analysis (CCA). The Procrustes formulation offers a general framework to link together variance analysis methods, and regression methods, including as special cases SVD and CCA.
Using this approach two fields can be divided into a subspace where variations of one field are connected to variations of the other field, the coupled manifold, and a subspace where variations are independent, the free manifold. The unified approach can be applied to prescribed SST experiments, in which case it recovers consistent results with other methods designed to identify the forced portion of variance, but it can now be extended also to the coupled case or to observations.
Some examples from prescribed SST simulation experiments and observations are discussed.
Abstract
The double–intertropical convergence zone (DI) systematic error, affecting state-of-the-art coupled general circulation models (CGCMs), is examined in the multimodel Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) ensemble of simulations of the twentieth-century climate. The aim of this study is to quantify the DI error on precipitation in the tropical Pacific, with a specific focus on the relationship between the DI error and the representation of large-scale vertical circulation regimes in climate models. The DI rainfall signal is analyzed using a regime-sorting approach for the vertical circulation regimes. Through the use of this compositing technique, precipitation events are regime sorted based on the large-scale vertical motions, as represented by the midtropospheric Lagrangian pressure tendency ω 500 dynamical proxy. This methodology allows partition of the precipitation signal into deep and shallow convective components. Following the regime-sorting diagnosis, the total DI bias is split into an error affecting the magnitude of precipitation associated with individual convective events and an error affecting the frequency of occurrence of single convective regimes. It is shown that, despite the existing large intramodel differences, CGCMs can be ultimately grouped into a few homogenous clusters, each featuring a well-defined rainfall–vertical circulation relationship in the DI region. Three major behavioral clusters are identified within the AR4 models ensemble: two unimodal distributions, featuring maximum precipitation under subsidence and deep convection regimes, respectively, and one bimodal distribution, displaying both components. Extending this analysis to both coupled and uncoupled (atmosphere only) AR4 simulations reveals that the DI bias in CGCMs is mainly due to the overly frequent occurrence of deep convection regimes, whereas the error on rainfall magnitude associated with individual convective events is overall consistent with errors already present in the corresponding atmosphere stand-alone simulations. A critical parameter controlling the strength of the DI systematic error is identified in the model-dependent sea surface temperature (SST) threshold leading to the onset of deep convection (THR), combined with the average SST in the southeastern Pacific. The models featuring a THR that is systematically colder (warmer) than their mean surface temperature are more (less) prone to exhibit a spurious southern intertropical convergence zone.
Abstract
The double–intertropical convergence zone (DI) systematic error, affecting state-of-the-art coupled general circulation models (CGCMs), is examined in the multimodel Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) ensemble of simulations of the twentieth-century climate. The aim of this study is to quantify the DI error on precipitation in the tropical Pacific, with a specific focus on the relationship between the DI error and the representation of large-scale vertical circulation regimes in climate models. The DI rainfall signal is analyzed using a regime-sorting approach for the vertical circulation regimes. Through the use of this compositing technique, precipitation events are regime sorted based on the large-scale vertical motions, as represented by the midtropospheric Lagrangian pressure tendency ω 500 dynamical proxy. This methodology allows partition of the precipitation signal into deep and shallow convective components. Following the regime-sorting diagnosis, the total DI bias is split into an error affecting the magnitude of precipitation associated with individual convective events and an error affecting the frequency of occurrence of single convective regimes. It is shown that, despite the existing large intramodel differences, CGCMs can be ultimately grouped into a few homogenous clusters, each featuring a well-defined rainfall–vertical circulation relationship in the DI region. Three major behavioral clusters are identified within the AR4 models ensemble: two unimodal distributions, featuring maximum precipitation under subsidence and deep convection regimes, respectively, and one bimodal distribution, displaying both components. Extending this analysis to both coupled and uncoupled (atmosphere only) AR4 simulations reveals that the DI bias in CGCMs is mainly due to the overly frequent occurrence of deep convection regimes, whereas the error on rainfall magnitude associated with individual convective events is overall consistent with errors already present in the corresponding atmosphere stand-alone simulations. A critical parameter controlling the strength of the DI systematic error is identified in the model-dependent sea surface temperature (SST) threshold leading to the onset of deep convection (THR), combined with the average SST in the southeastern Pacific. The models featuring a THR that is systematically colder (warmer) than their mean surface temperature are more (less) prone to exhibit a spurious southern intertropical convergence zone.
Abstract
This study investigates the possible changes that greenhouse global warming might generate in the characteristics of tropical cyclones (TCs). The analysis has been performed using scenario climate simulations carried out with a fully coupled high-resolution global general circulation model. The capability of the model to reproduce a reasonably realistic TC climatology has been assessed by comparing the model results from a simulation of the twentieth century with observations. The model appears to be able to simulate tropical cyclone–like vortices with many features similar to the observed TCs. The simulated TC activity exhibits realistic geographical distribution, seasonal modulation, and interannual variability, suggesting that the model is able to reproduce the major basic mechanisms that link TC occurrence with large-scale circulation. The results from the climate scenarios reveal a substantial general reduction of TC frequency when the atmospheric CO2 concentration is doubled and quadrupled. The reduction appears particularly evident for the tropical western North Pacific (WNP) and North Atlantic (ATL). In the NWP the weaker TC activity seems to be associated with reduced convective instabilities. In the ATL region the weaker TC activity seems to be due to both the increased stability of the atmosphere and a stronger vertical wind shear. Despite the generally reduced TC activity, there is evidence of increased rainfall associated with the simulated cyclones. Finally, the action of the TCs remains well confined to the tropical region and the peak of TC number remains equatorward of 20° latitude in both hemispheres, notwithstanding the overall warming of the tropical upper ocean and the expansion poleward of warm SSTs.
Abstract
This study investigates the possible changes that greenhouse global warming might generate in the characteristics of tropical cyclones (TCs). The analysis has been performed using scenario climate simulations carried out with a fully coupled high-resolution global general circulation model. The capability of the model to reproduce a reasonably realistic TC climatology has been assessed by comparing the model results from a simulation of the twentieth century with observations. The model appears to be able to simulate tropical cyclone–like vortices with many features similar to the observed TCs. The simulated TC activity exhibits realistic geographical distribution, seasonal modulation, and interannual variability, suggesting that the model is able to reproduce the major basic mechanisms that link TC occurrence with large-scale circulation. The results from the climate scenarios reveal a substantial general reduction of TC frequency when the atmospheric CO2 concentration is doubled and quadrupled. The reduction appears particularly evident for the tropical western North Pacific (WNP) and North Atlantic (ATL). In the NWP the weaker TC activity seems to be associated with reduced convective instabilities. In the ATL region the weaker TC activity seems to be due to both the increased stability of the atmosphere and a stronger vertical wind shear. Despite the generally reduced TC activity, there is evidence of increased rainfall associated with the simulated cyclones. Finally, the action of the TCs remains well confined to the tropical region and the peak of TC number remains equatorward of 20° latitude in both hemispheres, notwithstanding the overall warming of the tropical upper ocean and the expansion poleward of warm SSTs.
Abstract
ENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea surface temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated for both observations and a simulation from a GCM. This could be useful for understanding and diagnosing general circulation models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon. The index correctly identifies the emergence of the spring predictability barrier and the seasonal variations of the transition probabilities. The transition probability matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.
Abstract
ENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea surface temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated for both observations and a simulation from a GCM. This could be useful for understanding and diagnosing general circulation models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon. The index correctly identifies the emergence of the spring predictability barrier and the seasonal variations of the transition probabilities. The transition probability matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.
Abstract
The understanding of the dynamics of the El Niño–La Niña phenomenon in the tropical Pacific has been the subject of an impressive number of works in the last 20 years. The delayed oscillator theory provides an interpretative framework that has allowed enormous advances in understanding. Much evidence that stochastic forcing does play a role in the dynamics of ENSO has been discussed and it is possible to shape a theory of El Niño as a stochastically forced linear system. However, it is still uncertain if El Niño is a self-sustained nonlinear oscillatory system, a chaotic system, or a stochastically forced linear system. The authors propose in this paper that it is possible to have realistic El Niño probability distributions assuming that the system is a nonlinear stochastically forced system. In this paper a simple system is proposed that retains the main characteristics of the El Niño–La Niña variations, such as the skewness and the autocorrelation, and it is shown how solutions for the probability distribution can be obtained using a Fokker–Planck equation.
Abstract
The understanding of the dynamics of the El Niño–La Niña phenomenon in the tropical Pacific has been the subject of an impressive number of works in the last 20 years. The delayed oscillator theory provides an interpretative framework that has allowed enormous advances in understanding. Much evidence that stochastic forcing does play a role in the dynamics of ENSO has been discussed and it is possible to shape a theory of El Niño as a stochastically forced linear system. However, it is still uncertain if El Niño is a self-sustained nonlinear oscillatory system, a chaotic system, or a stochastically forced linear system. The authors propose in this paper that it is possible to have realistic El Niño probability distributions assuming that the system is a nonlinear stochastically forced system. In this paper a simple system is proposed that retains the main characteristics of the El Niño–La Niña variations, such as the skewness and the autocorrelation, and it is shown how solutions for the probability distribution can be obtained using a Fokker–Planck equation.
Abstract
In this paper results from the application of an ocean data assimilation (ODA) system, combining a multivariate reduced-order optimal interpolator (OI) scheme with a global ocean general circulation model (OGCM), are described. The present ODA system, designed to assimilate in situ temperature and salinity observations, has been used to produce ocean reanalyses for the 1962–2001 period. The impact of assimilating observed hydrographic data on the ocean mean state and temporal variability is evaluated. A special focus of this work is on the ODA system skill in reproducing a realistic ocean salinity state. Results from a hierarchy of different salinity reanalyses, using varying combinations of assimilated data and background error covariance structures, are described. The impact of the space and time resolution of the background error covariance parameterization on salinity is addressed.
Abstract
In this paper results from the application of an ocean data assimilation (ODA) system, combining a multivariate reduced-order optimal interpolator (OI) scheme with a global ocean general circulation model (OGCM), are described. The present ODA system, designed to assimilate in situ temperature and salinity observations, has been used to produce ocean reanalyses for the 1962–2001 period. The impact of assimilating observed hydrographic data on the ocean mean state and temporal variability is evaluated. A special focus of this work is on the ODA system skill in reproducing a realistic ocean salinity state. Results from a hierarchy of different salinity reanalyses, using varying combinations of assimilated data and background error covariance structures, are described. The impact of the space and time resolution of the background error covariance parameterization on salinity is addressed.
