David L. T. Anderson

The ECMWF Ocean Analysis System: ORA-S3

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 assimilatio...

Global seasonal rainfall forecasts using a coupled ocean–atmosphere model

One conceptual model of weather is that of a series of events which are unconnected. That is, that the weather next week is essentially independent of the weather this week. However, although individual weather systems might be chaotic and unpredictable beyond a week or so, the statistics describing them may be perturbed in a deterministic and predictable way, particularly by the ocean. In the past, seasonal forceasts of atmospheric variables have largely been based on empirical relationships, which are weak in most areas of the world. More recently, atmosphere models forced by assumed or predicted ocean conditions have been used,. Here a fully coupled global ocean–atmosphere general circulation model is used to make seasonal forecasts of the climate system with a lead time of up to 6 months. Such a model should be able to simulate the predictable perturbations of seasonal climate, but to extract these from the chaotic weather requires an ensemble of model integrations, and hence considerable computer resources. Reliable verification of probabilistic forecasts is difficult, but the results obtained so far, when compared to observations, are encouraging for the prospects for seasonal forecasting. Rainfall predictions for 1997 and the first half of 1998 show a marked increase in the spatial extent of statistically significant anomalies during the present El Niño, and include strong signals over Europe.

Dynamics of the Eastern Surface Jets in the Equatorial Indian Ocean

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 2‰-layer to 4‰-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.

An Ensemble Generation Method for Seasonal Forecasting with an Ocean–Atmosphere Coupled Model

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.

Seasonal Forecasting of Tropical Cyclone Landfall over Mozambique

Abstract The 2000 tropical cyclone season over the South Indian Ocean (SIO) was exceptional in terms of tropical cyclone landfall over Mozambique. Observed data suggest that SIO tropical cyclones have a track significantly more zonal during a La Nina event and tend to be more frequent when local SSTs are warmer. The combination of both conditions happened during the 2000 SIO tropical cyclone season and may explain the exceptional number of tropical cyclone landfalls over Mozambique during that season. A set of experiments using an atmospheric model of fairly high resolution (TL159, with a Gaussian grid spacing of 1.125°) forced by prescribed SSTs confirms the role of La Nina conditions and warmer local SSTs on the frequency of tropical cyclone landfalls over Mozambique. This also suggests that a numerical model can simulate the mechanisms responsible for the exceptional 2000 tropical cyclone season, and therefore could be used to explicitly predict the risk of landfall over Mozambique. A coupled model wit...

Salinity Adjustments in the Presence of Temperature Data Assimilation

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 str...

Did the ECMWF seasonal forecast model outperform statistical ENSO forecast models over the last 15 years

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 Nino–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 Nino or La Nina in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

Seasonal Transport Variations in the Florida Straits: A Model Study

Abstract In a previous study Anderson and Corry used a wind-driven two-layer model to study the effects of topography and islands on the seasonal variation of western boundary currents. The work is continued here with topography, geography and winds appropriate to the North Atlantic to examine the seasonal cycle of the Florida Straits transport. A summer maximum of transport is predicted consistent with observations. The area of importance and processes giving rise to the seasonal cycle are considered.

Data assimilation in ocean models

This review covers recent advances in applying data assimilation techniques to problems in physical oceanography. The introduction and appendices provide the non-specialist reader with background in ocean circulation and observing methods. The 4D variational assimilation approach is covered in depth showing how model - data misfits can be minimized using Lagrange multipliers in an unconstrained variational problem. Applications to modelling tropical Pacific temperatures, sea surface heights and circulation in the North Atlantic, and tomographic (sound travel time) data are all presented. The use of variational methods for deriving average climatological circulation patterns is also described as well as applications to error growth during numerical forecasting. The paper then focuses on three important topics in physical oceanography, the evolution of ocean mesoscale eddies in middle latitudes, the development of El Nino events in the tropical Pacific, and the evolution of ocean surface waves. Recent improvements in data acquisition and modelling in these areas mean that data assimilation is practical and is providing new insights and forecasting capabilities to varying degrees. Results using a variety of assimilation techniques are presented, concluding with a forward look to a time when routine forecasting of ocean developments will be possible with important implications ranging from understanding climate change to fishing and pollution control.

Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF operational ocean reanalysis

A reconstruction of the Atlantic Meridional Overturning Circulation (MOC) for theperiod 1959-2006 has been derived from the ECMWF operational ocean reanalysis. The reconstruction shows a wide range of time-variability, including a downward trend. At 26N, both the MOC intensity and changes in its vertical structure are in good agreement with previous estimates based on trans-Atlantic surveys. At 50N, the MOC and strength of the subpolar gyre are correlated at interannual time scales, but show opposite secular trends. Heat transport variability is highly correlated with the MOC but shows a smaller trend due to the warming of the upper ocean, which partially compensates for the weakening of the circulation. Results from sensitivity experiments show that although the time-varying upper boundary forcing provides useful MOC information, the sequential assimilation of ocean data further improves the MOC estimation by increasing both the mean and the time variability.