Search Results
Abstract
When forecasting sea surface temperature (SST) in the Equatorial Pacific on a timescale of several seasons, most prediction schemes have a spring barrier; that is, they have skill scores that are substantially lower when predicting northern spring and summer conditions compared to autumn and winter. This feature is investigated by examining predictions during the 1970s and the 1980s, using a dynamic ocean model of intermediate complexity coupled to a statistical atmosphere. Results show that predictions initialized during the 1970s exhibit the typical prominent skill decay in spring, whereas the seasonal dependence in those predictions initialized during the 1980s is rather small. Similar changes in seasonal dependence are also found in predictions based on simple persistence of observed SST anomalies.
This decadal change in the spring barrier is related to decadal variations found in the seasonal phase locking of the SST anomalies, which is largely determined by the timing of El Niño events. The spring barrier was strong in the 1970s, when El Niño was strongly phaselocked to the annual cycle. An analysis of observed SST anomalies from 1900 to 1990 shows several changes in behavior on a decadal scale, with the largest change being from the 1970s to the 1980s.
The seasonal dependence of model heat content predictions is investigated and found to be similar to that for SST, except that it shows a winter barrier rather than the spring barrier evident in SST.
Abstract
When forecasting sea surface temperature (SST) in the Equatorial Pacific on a timescale of several seasons, most prediction schemes have a spring barrier; that is, they have skill scores that are substantially lower when predicting northern spring and summer conditions compared to autumn and winter. This feature is investigated by examining predictions during the 1970s and the 1980s, using a dynamic ocean model of intermediate complexity coupled to a statistical atmosphere. Results show that predictions initialized during the 1970s exhibit the typical prominent skill decay in spring, whereas the seasonal dependence in those predictions initialized during the 1980s is rather small. Similar changes in seasonal dependence are also found in predictions based on simple persistence of observed SST anomalies.
This decadal change in the spring barrier is related to decadal variations found in the seasonal phase locking of the SST anomalies, which is largely determined by the timing of El Niño events. The spring barrier was strong in the 1970s, when El Niño was strongly phaselocked to the annual cycle. An analysis of observed SST anomalies from 1900 to 1990 shows several changes in behavior on a decadal scale, with the largest change being from the 1970s to the 1980s.
The seasonal dependence of model heat content predictions is investigated and found to be similar to that for SST, except that it shows a winter barrier rather than the spring barrier evident in SST.
Abstract
Many features of the El Niño–Southern Oscillation (ENSO) phenomenon have been successfully simulated by coupled models during the last decade; however, some fundamental differences in model behavior remain. They can be classified into two categories according to whether the oscillation is self-sustained within the Pacific sector or whether some external impacts are needed to maintain the oscillation. In the first category, the delayed oscillator scenario describes ENSO as an oscillation generated and maintained by the coupled instability and oceanic waves, without the need for any external impacts. In the second category, the system has two steady states of equilibrium and an external forcing is needed to move the system from one state to another. Recent observational analyses suggest possible interactions or connections between external influences and ENSO variability.
The effects of external impacts on ENSO variability are investigated here by using a simple coupled ocean–atmosphere model. The impacts considered are wind-stress anomalies associated with the seasonal monsoonal cycle, and the tropospheric quasi-biennial oscillation in the Indian and western Pacific region. It was found that 1) the external impact plays an important role in triggering ENSO variability when the coupled system in the Pacific could not support the oscillation by itself, 2) the impact regulates the original self-sustained oscillation to a seasonally phase-locked time evolution; and 3) the periods of the resulting oscillations could be three times that of the external forcing, a result of the interaction between the external forcing and the coupled system in the Pacific.
A modified version of the delayed oscillator equation was used to examine further details of the interaction. It was found that the match of half of the period of the external forcing with the delay time of the reflected oceanic waves from the western boundary arriving at the air–sea interaction region to turn off an event is a key factor in determining how they interact. If the time-matching condition is satisfied, the oscillation period will be three times that of the forcing. It is also shown that wind stress associated with the quasi-biennial oscillation could influence significantly the original self-sustained oscillation in the Pacific, making the amplitude and interval between two successive warm or cold phases variable, as observed in ENSO events.
Abstract
Many features of the El Niño–Southern Oscillation (ENSO) phenomenon have been successfully simulated by coupled models during the last decade; however, some fundamental differences in model behavior remain. They can be classified into two categories according to whether the oscillation is self-sustained within the Pacific sector or whether some external impacts are needed to maintain the oscillation. In the first category, the delayed oscillator scenario describes ENSO as an oscillation generated and maintained by the coupled instability and oceanic waves, without the need for any external impacts. In the second category, the system has two steady states of equilibrium and an external forcing is needed to move the system from one state to another. Recent observational analyses suggest possible interactions or connections between external influences and ENSO variability.
The effects of external impacts on ENSO variability are investigated here by using a simple coupled ocean–atmosphere model. The impacts considered are wind-stress anomalies associated with the seasonal monsoonal cycle, and the tropospheric quasi-biennial oscillation in the Indian and western Pacific region. It was found that 1) the external impact plays an important role in triggering ENSO variability when the coupled system in the Pacific could not support the oscillation by itself, 2) the impact regulates the original self-sustained oscillation to a seasonally phase-locked time evolution; and 3) the periods of the resulting oscillations could be three times that of the external forcing, a result of the interaction between the external forcing and the coupled system in the Pacific.
A modified version of the delayed oscillator equation was used to examine further details of the interaction. It was found that the match of half of the period of the external forcing with the delay time of the reflected oceanic waves from the western boundary arriving at the air–sea interaction region to turn off an event is a key factor in determining how they interact. If the time-matching condition is satisfied, the oscillation period will be three times that of the forcing. It is also shown that wind stress associated with the quasi-biennial oscillation could influence significantly the original self-sustained oscillation in the Pacific, making the amplitude and interval between two successive warm or cold phases variable, as observed in ENSO events.
Abstract
In this paper the structure and dynamics of the optimal perturbations of tropical low-frequency coupled ocean–atmosphere oscillations relevant to El Niño–Southern Oscillation (ENSO) are explored. These optimal perturbations yield information about potential precursors for ENSO events, and about the fundamental dynamical processes that may control perturbation growth and limit the predictability of interannual variability. The present study uses a hierarchy of hybrid coupled models. Each model is configured for the tropical Pacific Ocean and shares a common ocean general circulation model. Three different atmospheric models are used: a statistical model, a dynamical model, and a combination of a dynamical model and boundary layer model. Each coupled model possesses a coupled ocean–atmosphere eigenmode oscillation with a period of the order of several years. The properties of these various eigenmodes and their corresponding adjoint eigenmodes are explored.
The optimal perturbations of each coupled model for two different perturbation growth norms are also examined, and their behavior can be understood in terms of the properties of the aforementioned eigenmode oscillations. It is found that the optimal perturbation spectrum of each coupled model is primarily dominated by one member. The dominant optimal perturbation evolves into the most unstable eigenmode of the system. The structure of the optimal perturbations of each model is found to be controlled by the dynamics of the atmospheric model and air–sea interaction processes. For the coupled model with a statistical atmosphere, the optimal perturbation center of action is spread across the entire tropical Pacific in the form of a dipole. For the coupled models that include deep atmospheric convection, the optimal perturbation center of action is primarily confined to the western Pacific warm pool. In addition, the degree of nonnormality of the eigenmodes is controlled by the atmospheric model dynamics. These findings are in general agreement with the results obtained from intermediate coupled models. In particular, the atmospheric models used here have also been used in intermediate coupled models that have been employed extensively in previous studies of the optimal perturbations of El Niño–Southern Oscillation. Thus, a direct comparison of the optimal perturbation behavior of those intermediate models and the optimal perturbations of the hybrid models used here can be made.
Abstract
In this paper the structure and dynamics of the optimal perturbations of tropical low-frequency coupled ocean–atmosphere oscillations relevant to El Niño–Southern Oscillation (ENSO) are explored. These optimal perturbations yield information about potential precursors for ENSO events, and about the fundamental dynamical processes that may control perturbation growth and limit the predictability of interannual variability. The present study uses a hierarchy of hybrid coupled models. Each model is configured for the tropical Pacific Ocean and shares a common ocean general circulation model. Three different atmospheric models are used: a statistical model, a dynamical model, and a combination of a dynamical model and boundary layer model. Each coupled model possesses a coupled ocean–atmosphere eigenmode oscillation with a period of the order of several years. The properties of these various eigenmodes and their corresponding adjoint eigenmodes are explored.
The optimal perturbations of each coupled model for two different perturbation growth norms are also examined, and their behavior can be understood in terms of the properties of the aforementioned eigenmode oscillations. It is found that the optimal perturbation spectrum of each coupled model is primarily dominated by one member. The dominant optimal perturbation evolves into the most unstable eigenmode of the system. The structure of the optimal perturbations of each model is found to be controlled by the dynamics of the atmospheric model and air–sea interaction processes. For the coupled model with a statistical atmosphere, the optimal perturbation center of action is spread across the entire tropical Pacific in the form of a dipole. For the coupled models that include deep atmospheric convection, the optimal perturbation center of action is primarily confined to the western Pacific warm pool. In addition, the degree of nonnormality of the eigenmodes is controlled by the atmospheric model dynamics. These findings are in general agreement with the results obtained from intermediate coupled models. In particular, the atmospheric models used here have also been used in intermediate coupled models that have been employed extensively in previous studies of the optimal perturbations of El Niño–Southern Oscillation. Thus, a direct comparison of the optimal perturbation behavior of those intermediate models and the optimal perturbations of the hybrid models used here can be made.
Abstract
The optimal forcing patterns for El Niño–Southern Oscillation (ENSO) are examined for a hierarchy of hybrid coupled models using generalized stability theory. Specifically two cases are considered: one where the forcing is stochastic in time, and one where the forcing is time independent. The optimal forcing patterns in these two cases are described by the stochastic optimals and forcing singular vectors, respectively. The spectrum of stochastic optimals for each model was found to be dominated by a single pattern. In addition, the dominant stochastic optimal structure is remarkably similar to the forcing singular vector, and to the dominant singular vectors computed in a previous related study using a subset of the same models. This suggests that irrespective of whether the forcing is in the form of an impulse, is time invariant, or is stochastic in nature, the optimal excitation for the eigenmode that describes ENSO in each model is the same. The optimal forcing pattern, however, does vary from model to model, and depends on air–sea interaction processes.
Estimates of the stochastic component of forcing were obtained from atmospheric analyses and the projection of the dominant optimal forcing pattern from each model onto this component of the forcing was computed. It was found that each of the optimal forcing patterns identified may be present in nature and all are equally likely. The existence of a dominant optimal forcing pattern is explored in terms of the effective dimension of the coupled system using the method of balanced truncation, and was found to be O(1) for the models used here. The implications of this important result for ENSO prediction and predictability are discussed.
Abstract
The optimal forcing patterns for El Niño–Southern Oscillation (ENSO) are examined for a hierarchy of hybrid coupled models using generalized stability theory. Specifically two cases are considered: one where the forcing is stochastic in time, and one where the forcing is time independent. The optimal forcing patterns in these two cases are described by the stochastic optimals and forcing singular vectors, respectively. The spectrum of stochastic optimals for each model was found to be dominated by a single pattern. In addition, the dominant stochastic optimal structure is remarkably similar to the forcing singular vector, and to the dominant singular vectors computed in a previous related study using a subset of the same models. This suggests that irrespective of whether the forcing is in the form of an impulse, is time invariant, or is stochastic in nature, the optimal excitation for the eigenmode that describes ENSO in each model is the same. The optimal forcing pattern, however, does vary from model to model, and depends on air–sea interaction processes.
Estimates of the stochastic component of forcing were obtained from atmospheric analyses and the projection of the dominant optimal forcing pattern from each model onto this component of the forcing was computed. It was found that each of the optimal forcing patterns identified may be present in nature and all are equally likely. The existence of a dominant optimal forcing pattern is explored in terms of the effective dimension of the coupled system using the method of balanced truncation, and was found to be O(1) for the models used here. The implications of this important result for ENSO prediction and predictability are discussed.
Abstract
The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.
Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.
Abstract
The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.
Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.
Abstract
Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.
The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence.
As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections.
The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.
Abstract
Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.
The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence.
As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections.
The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.
