Search Results

You are looking at 1 - 10 of 29 items for

  • Author or Editor: D. L. T. Anderson x
  • Refine by Access: All Content x
Clear All Modify Search
D. L. T. Anderson and P. F. Noar

Abstract

A detailed examination has been made of the synoptic and physical behavior of an initially diffluent, large-amplitude but representative trough that formed in a Southern Hemisphere general circulation model after approximately 60 model days. The model is essentially the stereographic GFDL model of Manabe et al. (1965) with 9 levels and 30 points between equator and pole.

The trough developed in a manner characteristic of the real atmosphere in that a jet maximum moved round to the apex of the trough, then to the leading edge. However, a baroclinic zone persisting in the rear of the trough prevented collapse of the cold air and the release of eddy kinetic energy. Vertical velocity fields were partitioned into components associated with differential vorticity advection and the Laplacian of thermal advection, and comparisons made with Krishnamurti's (1968) results. It is suggested that model developments are too strongly controlled by the vorticity and thermal terms.

The model polar-front jet was located at a level close to the subtropical jet and was overshadowed by it. Trajectory analyses showed that despite this discrepancy, little interaction occurred between the two major jet streams.

Full access
J. Segschneider, D. L. T. Anderson, and T. N. Stockdale

Abstract

The TOPEX/Poseidon and ERS-1/2 satellites have now been observing sea level anomalies for a continuous time span of more than 6 yr. These sea level observations are first compared with tide gauge data and then assimilated into an ocean model that is used to initialize coupled ocean–atmosphere forecasts with a lead time of 6 months. Ocean analyses in which altimeter data are assimilated are compared with those from a no-assimilation experiment and with analyses in which subsurface temperature observations are assimilated. Analyses with altimeter data show variations of upper-ocean heat content similar to analyses using subsurface observations, whereas the ocean model has large errors when no data are assimilated. However, obtaining good results from the assimilation of altimeter data is not straightforward: it is essential to add a good mean sea level to the observed anomalies, to filter the sea level observations appropriately, to start the analyses from realistic initial temperature and salinity fields, and to assign appropriate weights for the analyzed increments.

To assess the impact of altimeter data assimilation on the coupled system, ensemble hindcasts are initialized from ocean analyses in which either no data, subsurface temperatures, or sea level observations were assimilated. For each kind of ocean analysis, a five-member ensemble is started every 3 months from January 1993 to October 1997, adding up to 100 forecasts for each type. The predicted SST anomalies for the equatorial Pacific are intercompared between the experiments and against observations. The predicted anomalies are on average closer to observed values when forecasts are initialized from the ocean analysis using altimeter data than when initialized from the no-assimilation ocean analysis, and forecast errors appear to be only slightly larger than for forecasts initialized from ocean analyses using subsurface temperatures. However, even based on 100 coupled forecasts, the distinction between the two experiments that benefit from data assimilation is barely statistically significant. The verification should still be considered preliminary, because the period covered by the forecasts is only 5 yr, which is too short properly to sample ENSO variability. It is, nonetheless, encouraging that altimeter assimilation can improve the forecast skill to a level comparable to that obtained from using Tropical Ocean Atmosphere–expendable bathythermograph data.

Full access
A. T. Weaver, J. Vialard, and D. L. T. Anderson

Abstract

Three- and four-dimensional variational assimilation (3DVAR and 4DVAR) systems have been developed for the Océan Parallélisé (OPA) ocean general circulation model (OGCM) of the Laboratoire d'Océanographie Dynamique et de Climatologie. An iterative incremental approach is used to minimize a cost function that measures the statistically weighted squared differences between the observational information and their model equivalent. The control variable of the minimization problem is an increment to the background estimate of the model initial conditions at the beginning of each assimilation window. In 3DVAR, the increment is transported between observation times within the window using a persistence model, while in 4DVAR a dynamical model derived from the tangent linear (TL) of the OGCM is used. Both the persistence and TL models are shown to provide reasonably good descriptions of the evolution of typical errors over the 10- and 30-day widths of the assimilation windows used in the authors' 3DVAR and 4DVAR experiments, respectively.

The present system relies on a univariate formulation of the background-error covariance matrix. In practice, the background-error covariances are specified implicitly within a change of control variable designed to improve the conditioning of the minimization problem. Horizontal and vertical correlation functions are modeled using a filter based on a numerical integration of a diffusion equation. The background-error variances are geographically dependent and specified from the model climatology. Single observation experiments are presented to illustrate how the TL dynamics act to modify these variances in a flow-dependent way by diminishing their values in the mixed layer and by displacing the maximum value of the variance to the level of the background thermocline.

The 3DVAR and 4DVAR systems have been applied to a tropical Pacific version of OPA and cycled over the period 1993–98 using in situ temperature observations from the Global Temperature and Salinity Pilot Programme. The overall effect of the data assimilation is to reduce a large bias in the thermal field, which was present in the control. The fit to the data in 4DVAR is better than in 3DVAR, and within the specified observation-error standard deviation. Intermittent updating of the linearization state of the TL model is shown to be an important feature of the incremental 4DVAR algorithm and contributes significantly to improving the fit to the data.

Full access
J. O. S. Alves, K. Haines, and D. L. T. Anderson

Abstract

Idealized twin experiments with the HOPE ocean model have been used to study the ability of sea level data assimilation to correct for errors in a model simulation of the tropical Pacific, using the Cooper and Haines method to project the surface height increments below the surface. This work should be seen in the context of the development of the comprehensive real-time ocean analysis system used at ECMWF for seasonal forecasting, which currently assimilates only thermal data.

Errors in the model simulation from two sources are studied: those present in the initial state and those generated by errors in the surface forcing during the simulation. In the former, the assimilation of sea level data improves the convergence of the model toward its twin. Without assimilation convergence occurs more slowly on the equator, compared to an experiment using only correct surface forcing. With forcing errors present the sea level assimilation still significantly reduces the errors almost everywhere. An exception was in the central equatorial Pacific where assimilation of sea level did not correct the errors. This is mainly due to this region responding rapidly to errors in wind stress forcing and also to relatively large freshwater flux errors imposed here. These lead to errors in the mixed layer salinity, which the Cooper and Haines scheme is not designed to correct. It is argued that surface salinity analyses would strongly complement sea level assimilation here.

Full access
M. K. Davey, D. L. T. Anderson, and S. Lawrence

Abstract

In many prediction schemes, the skill of long-range forecasts of ENSO events depends on the time of year. Such variability could be directly due to seasonal changes in the basic ocean-atmosphere system or due to the state of ENSO itself.

A highly idealized delayed oscillator model with seasonally varying internal parameters is used here to simulate such behavior. The skill of the artificial forecasts shows dependence on both seasonal and ENSO phase. Experiments with ENSO phase-locked to the seasonal cycle. but with no seasonal variation of model parameters. show that the ENSO cycle alone can induce variability in skill. Inclusion of seasonal parameters enhances seasonal skill dependence. It is suggested that the seasonal skill variations found in practice am due to a combination of seasonal changes in the basic state and the phase-locking of the ENSO and annual cycles.

Full access
A. M. Moore, N. S. Cooper, and D. L. T. Anderson

Abstract

Numerical experiments have been conducted to investigate the effect of updating models of the Indian Ocean using simulated temperature (mass) and velocity data. Two models are used: a linear reduced gravity model with one active layer, and a nonlinear 12-level general circulation model (GCM). In both cases an “identical twin” approach is adopted, in which the same model is used to generate the “observed” data in a “truth run”, as is used in the assimilation run.

Temperature data is found to be better than velocity data for initializing both models. However, further experiments with the layer model showed that increasing the model diffusion and decreasing the eddy viscosity results in velocity data being better for initializing. These results are ascribed to the energy distribution, with the proportion of kinetic energy being greater in the later experiments.

Simulated data from the proposed TOGA Indian Ocean XBT network were also assimilated into both models using a successive correction interpolation scheme. It is found that for the layer model, which had smooth horizontal variations in thermocline depth, the errors fall to zero within a couple of months. However, in the experiments with the GCM there is little reduction in the assimilation error after the first model update, due to the data analysis scheme not being able to resolve the horizontal temperature structure in the GCM.

Full access
A. Vidard, D. L. T. Anderson, and M. Balmaseda

Abstract

The relative merits of the Tropical Atmosphere–Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TAO/TRITON) and Pilot Research Moored Array in the Tropical Atlantic mooring networks, the Voluntary Observing Ship (VOS) expendable bathythermograph (XBT) network, and the Argo float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS datasets are withheld from the assimilation. To estimate the impact on seasonal forecast skill, the set of ocean analyses is then used to initialize a corresponding set of coupled ocean–atmosphere model forecasts. A further set of experiments is conducted to assess the impact of the more recent Argo array. A key parameter for seasonal forecast initialization is the depth of the thermocline in the tropical Pacific. This depth is quite similar in all of the experiments that involve data assimilation, but withdrawing the TAO data has a bigger effect than withdrawing XBT data, especially in the eastern half of the basin. The forecasts mainly indicate that the TAO/TRITON in situ temperature observations are essential to obtain optimum forecast skill. They are best combined with XBT, however, because this results in better predictions for the west Pacific. Furthermore, the XBTs play an important role in the North Atlantic. The ocean data assimilation performs less well in the tropical Atlantic. This may be partly a result of not having adequate observations of salinity.

Full access
J. Vialard, A. T. Weaver, D. L. T. Anderson, and P. Delecluse

Abstract

Three- and four-dimensional variational assimilation (3DVAR and 4DVAR) systems have been developed for the Océan Parallélisé (OPA) ocean general circulation model of the Laboratoire d'Océanographie Dynamique et de Climatologie. They have been applied to a tropical Pacific version of OPA and cycled over the period 1993–98 using in situ temperature observations from the Global Temperature and Salinity Pilot Programme. The assimilation system is described in detail in Part I of this paper. In this paper, an evaluation of the physical properties of the analyses is undertaken. Experiments performed with a univariate optimal interpolation (OI) scheme give similar results to those obtained with the univariate 3DVAR and are thus not discussed in detail. For the 3DVAR and 4DVAR, it is shown that both the mean state and interannual variability of the thermal field are improved by the assimilation. The fit to the assimilated data in 4DVAR is also very good at timescales comparable to or shorter than the 30-day assimilation window (e.g., at the timescale of tropical instability waves), which demonstrates the effectiveness of the linearized ocean dynamics in carrying information through time. Comparisons with data that are not assimilated are also presented. The intensity of the North Equatorial Counter Current is increased (and improved) in both assimilation experiments. A large eastward bias in the surface currents appears in the eastern Pacific in the 3DVAR analyses, but not in those of 4DVAR. The large current bias is related to a spurious vertical circulation cell that develops along the equatorial strip in 3DVAR. In 4DVAR, the surface current variability is moderately improved. The salinity displays a drift in both experiments but is less accentuated in 4DVAR than in 3DVAR. The better performance of 4DVAR is attributed to multivariate aspects of the 4DVAR analysis coming from the use of the linearized ocean dynamics as a constraint. Even in 4DVAR, however, additional constraints seem necessary to provide better control of the analysis of currents and salinity when observations of those variables are not directly assimilated. Improvements to the analysis can be expected in the future with the inclusion of a multivariate background-error covariance matrix. This and other possible ways of improving the analysis system are discussed.

Full access
Yun Fan, M. R. Allen, D. L. T. Anderson, and M. A. Balmaseda

Abstract

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.

Full access
Weiqing Han, Julian P. McCreary Jr., D. L. T. Anderson, and Arthur J. Mariano

Abstract

An hierarchy of ocean models is used to investigate the dynamics of the eastward surface jets that develop along the Indian Ocean equator during the spring and fall, the Wyrtki jets (WJs). The models vary in dynamical complexity from 2½-layer to 4½-layer systems, the latter including active thermodynamics, mixed layer physics, and salinity. To help identify processes, both linear and nonlinear solutions are obtained at each step in the hierarchy. Specific processes assessed are as follows: direct forcing by the wind, reflected Rossby waves, resonance, mixed layer shear, salinity effects, and the influence of the Maldive Islands. In addition, the sensitivity of solutions to forcing by different wind products is reported.

Consistent with previous studies, the authors find that direct forcing by the wind is the dominant forcing mechanism of the WJs, accounting for 81% of their amplitude when there is a mixed layer. Reflected Rossby waves, resonance, and mixed layer shear are all necessary to produce jets with realistic strength and structure. Completely new results are that precipitation during the summer and fall considerably strengthens the fall WJ in the eastern ocean by thinning the mixed layer, and that the Maldive Islands help both jets to attain roughly equal strengths.

In both the ship-drift data and the authors’ “best” solution (i.e., the solution to the highest model in the authors’ hierarchy), the semiannual response is more than twice as large as the annual one, even though the corresponding wind components have comparable amplitudes. Causes of this difference are as follows: the complex zonal structure of the annual wind, which limits the directly forced response at the annual frequency;resonance with the semiannual wind; and mixed layer shear flow, which interferes constructively (destructively) with the rest of the response for the semiannual (annual) component. Even in the most realistic solution, however, the annual component still weakens the fall WJ and strengthens the spring one in the central ocean, in contrast to the ship-drift data; this model/data discrepancy may result from model deficiencies, inaccurate driving winds, or from windage errors in the ship-drift data themselves.

Full access