Search Results
Abstract
The low-level jet which flows across the equator and up the Somali coast is considered as a western boundary current of the East African mountain chain. The jet is assumed to be forced by the low-level divergence in the subtropical high pressure belt of the Southern Hemisphere and convergence in the monsoon trough. A simple model with this type of forcing is proposed and analytic and numerical solutions obtained. These appear to be in reasonable agreement with observation. The sensitivity of the model jet to spatial variation in the forcing, temporal changes in the intensity of the low-level convergence, and nonlinearity are examined.
Abstract
The low-level jet which flows across the equator and up the Somali coast is considered as a western boundary current of the East African mountain chain. The jet is assumed to be forced by the low-level divergence in the subtropical high pressure belt of the Southern Hemisphere and convergence in the monsoon trough. A simple model with this type of forcing is proposed and analytic and numerical solutions obtained. These appear to be in reasonable agreement with observation. The sensitivity of the model jet to spatial variation in the forcing, temporal changes in the intensity of the low-level convergence, and nonlinearity are examined.
Abstract
A model of tropical ocean-atmosphere interaction is used to study the El Niño–Southern Oscillation phenomenon. The model ocean consists of the single baroclinic mode of a two-layer ocean. The thermodynamics of the upper layer are highly parameterized; sea-surface temperature is assigned one of two values, warm or cool, according to whether the interface is shallower or deeper than an externally specified depth. The model atmosphere consists of two wind patches of zonal stress that are idealizations of the annual cycle of the equatorial trades, τ s , and of Bjerknes' Walker circulation, τ w . When the eastern ocean is in its cool state both patches drive the ocean; when it is warm τ w is switched off. Solutions compare favorably with observations in several ways. Most importantly, for reasonable choices of parameters solutions oscillate at the long time scales associated with the Southern Oscillation.
The response of the ocean to τ w introduces positive feedback into the system, with the result that the system can adjust to one or the other of two equilibrium states: a state with τ w switched on, and another with it switched off. The annual wind τ s is the “trigger” that switches τ w off or on, and thereby prevents the system from ever reaching either equilibrium state.
When τ w switches on, equatorial Kelvin waves swiftly propagate from the wind patch into the eastern ocean, and raise the interface there to a shallow level. Rossby waves, also generated by the wind, subsequently reflect from the western boundary as a second set of equatorial Kelvin waves. The arrival of this second set in the eastern ocean begins a gradual deepening of the interface there toward its equilibrium value. It is this overshoot together with slow relaxation of the interface in the eastern ocean that allows the model to oscillate at long time scales. Essentially, the ocean must be sufficiently relaxed toward an equilibrium gate before τ s can act to switch τ w Off or on.
Abstract
A model of tropical ocean-atmosphere interaction is used to study the El Niño–Southern Oscillation phenomenon. The model ocean consists of the single baroclinic mode of a two-layer ocean. The thermodynamics of the upper layer are highly parameterized; sea-surface temperature is assigned one of two values, warm or cool, according to whether the interface is shallower or deeper than an externally specified depth. The model atmosphere consists of two wind patches of zonal stress that are idealizations of the annual cycle of the equatorial trades, τ s , and of Bjerknes' Walker circulation, τ w . When the eastern ocean is in its cool state both patches drive the ocean; when it is warm τ w is switched off. Solutions compare favorably with observations in several ways. Most importantly, for reasonable choices of parameters solutions oscillate at the long time scales associated with the Southern Oscillation.
The response of the ocean to τ w introduces positive feedback into the system, with the result that the system can adjust to one or the other of two equilibrium states: a state with τ w switched on, and another with it switched off. The annual wind τ s is the “trigger” that switches τ w off or on, and thereby prevents the system from ever reaching either equilibrium state.
When τ w switches on, equatorial Kelvin waves swiftly propagate from the wind patch into the eastern ocean, and raise the interface there to a shallow level. Rossby waves, also generated by the wind, subsequently reflect from the western boundary as a second set of equatorial Kelvin waves. The arrival of this second set in the eastern ocean begins a gradual deepening of the interface there toward its equilibrium value. It is this overshoot together with slow relaxation of the interface in the eastern ocean that allows the model to oscillate at long time scales. Essentially, the ocean must be sufficiently relaxed toward an equilibrium gate before τ s can act to switch τ w Off or on.
Abstract
A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF. The reanalysis, started from 1 January 1959, is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an online bias-correction algorithm, the assimilation of salinity data on temperature surfaces, and the assimilation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanalysis due to the nonstationary nature of the observing system, while still taking advantage of the observation information. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are improved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilation increment and bias correction terms. The resulting analysis not only improves the fit to the data, but also improves the representation of the interannual variability. In addition to the basic analysis, a real-time analysis is produced (RT-S3). This is needed for monthly forecasts and in the future may be needed for shorter-range forecasts. It is initialized from the near-real-time ORA-S3 and run each day from it.
Abstract
A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF. The reanalysis, started from 1 January 1959, is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an online bias-correction algorithm, the assimilation of salinity data on temperature surfaces, and the assimilation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanalysis due to the nonstationary nature of the observing system, while still taking advantage of the observation information. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are improved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilation increment and bias correction terms. The resulting analysis not only improves the fit to the data, but also improves the representation of the interannual variability. In addition to the basic analysis, a real-time analysis is produced (RT-S3). This is needed for monthly forecasts and in the future may be needed for shorter-range forecasts. It is initialized from the near-real-time ORA-S3 and run each day from it.
Abstract
The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions.
Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.
Abstract
The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions.
Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.
Abstract
Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.
Abstract
Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.
Abstract
This paper is an evaluation of the role of salinity in the framework of temperature data assimilation in a global ocean model that is used to initialize seasonal climate forecasts. It is shown that the univariate assimilation of temperature profiles, without attempting to correct salinity, can induce first-order errors in the subsurface temperature and salinity fields. A recently developed scheme by A. Troccoli and K. Haines is used to improve the salinity field. In this scheme, salinity increments are derived from the observed temperature, by using the model temperature and salinity profiles, assuming that the temperature–salinity relationship in the model profiles is preserved. In addition, the temperature and salinity fields are matched below the observed temperature profile by vertically displacing the original model profiles.
Two data assimilation experiments were performed for the 6-yr period 1993–98. These show that the salinity scheme is effective at maintaining the haline and thermal structures at and below thermocline level, especially in tropical regions, by avoiding spurious convection. In addition to improvements in the mean state, the scheme allows more temporal variability than simply controlling the salinity field by relaxation to climatological data. Some comparisons with sparse salinity observations are also made, which suggest that the subsurface salinity variability in the western Pacific is better reproduced in the experiment in which the salinity scheme is used. The salinity analyses might be improved further by use of altimeter sea level or sea surface salinity observations from satellite.
Abstract
This paper is an evaluation of the role of salinity in the framework of temperature data assimilation in a global ocean model that is used to initialize seasonal climate forecasts. It is shown that the univariate assimilation of temperature profiles, without attempting to correct salinity, can induce first-order errors in the subsurface temperature and salinity fields. A recently developed scheme by A. Troccoli and K. Haines is used to improve the salinity field. In this scheme, salinity increments are derived from the observed temperature, by using the model temperature and salinity profiles, assuming that the temperature–salinity relationship in the model profiles is preserved. In addition, the temperature and salinity fields are matched below the observed temperature profile by vertically displacing the original model profiles.
Two data assimilation experiments were performed for the 6-yr period 1993–98. These show that the salinity scheme is effective at maintaining the haline and thermal structures at and below thermocline level, especially in tropical regions, by avoiding spurious convection. In addition to improvements in the mean state, the scheme allows more temporal variability than simply controlling the salinity field by relaxation to climatological data. Some comparisons with sparse salinity observations are also made, which suggest that the subsurface salinity variability in the western Pacific is better reproduced in the experiment in which the salinity scheme is used. The salinity analyses might be improved further by use of altimeter sea level or sea surface salinity observations from satellite.
