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In 2010 eastern Australia received its highest springtime (September–November) rainfall since 1900. Based on historical relationships with sea surface temperatures (SST) and other climate indices, this record rainfall in 2010 was shown to be largely commensurate with the occurrence of a very strong La Nina event and an extreme positive excursion of the SAM. The pattern and magnitude of the tropical SST anomalies in austral spring 2010 were diagnosed to be nearly perfect to produce high rainfall across eastern Australia. Key aspects of this SST pattern were the strong cold anomaly in the central equatorial Pacific, and the strong warm anomalies in the eastern Indian Ocean and the far western Pacific to the north of Australia. Although the recent upward trend in SSTs in the western Pacific/eastern Indian Ocean warm pool accounted for about 50 % of the SST anomaly surrounding northern Australia in 2010, the contribution by the warming trend in these SSTs to the Australian rainfall anomaly in 2010 was assessed to be relatively modest. The strong positive swing in SAM was estimated to have accounted for upwards of 40 % of the regional anomaly along the central east coastal region and about 10 % of the area mean anomaly across eastern Australia. This contribution by the SAM suggests that a significant portion of the rainfall in 2010 may not have been seasonally predictable. However, predictability arising from the promotion of high SAM by the extreme La Nina event can not be ruled out.

The authors assess the sensitivity of the simulated mean state and coupled variability to systematic initial state salinity errors in seasonal forecasts using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) coupled model. This analysis is based on two sets of hindcasts that were initialized from old and new ocean initial conditions, respectively. The new ocean initial conditions are provided by an ensemble multivariate analysis system that assimilates subsurface temperatures and salinity and is a clear improvement over the previous system, which was based on univariate optimal interpolation, using static error covariances and assimilating only temperature without updating salinity. Large systematic errors in the salinity field around the thermocline region of the tropical western and central Pacific produced by the old assimilation scheme are shown to have strong impacts on the predicted mean state and variability in the tropical Pacific for the entire 9 months of the forecast. Forecasts initialized from the old scheme undergo a rapid and systematic adjustment of density that causes large persistent changes in temperature both locally in the western and central Pacific thermocline, but also remotely in the eastern Pacific via excitation of equatorial waves. The initial subsurface salinity errors in the western and central Pacific ultimately result in an altered surface climate because of induced temperature changes in the thermocline that trigger a coupled feedback in the eastern Pacific. These results highlight the importance of accurately representing salinity in initial conditions for climate prediction on seasonal and potentially multiyear time scales.

In light of the growing recognition of the role of surface temperature variations in the Indian Ocean for driving global climate variability, the predictive skill of the sea surface temperature (SST) anomalies associated with the Indian Ocean dipole (IOD) is assessed using ensemble seasonal forecasts from a selection of contemporary coupled climate models that are routinely used to make seasonal climate predictions. The authors assess predictions from successive versions of the Australian Bureau of Meteorology Predictive Ocean–Atmosphere Model for Australia (POAMA 15b and 24), successive versions of the NCEP Climate Forecast System (CFSv1 and CFSv2), the ECMWF seasonal forecast System 3 (ECSys3), and the Frontier Research Centre for Global Change system (SINTEX-F) using seasonal hindcasts initialized each month from January 1982 to December 2006. The lead time for skillful prediction of SST in the western Indian Ocean is found to be about 5–6 months while in the eastern Indian Ocean it is only 3–4 months when all start months are considered. For the IOD events, which have maximum amplitude in the September–November (SON) season, skillful prediction is also limited to a lead time of about one season, although skillful prediction of large IOD events can be longer than this, perhaps up to about two seasons. However, the tendency for the models to overpredict the occurrence of large events limits the confidence of the predictions of these large events. Some common model errors, including a poor representation of the relationship between El Niño and the IOD, are identified indicating that the upper limit of predictive skill of the IOD has not been achieved.

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3–6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.

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.

The importance of initializing atmospheric intra-seasonal (stochastic) variations for prediction of the onset of the 1997/1998 El Nino is examined using the Australian Bureau of Meteorology coupled seasonal forecast model. A suite of 9-month forecasts was initialized on the 1st December 1996. Observed ocean initial conditions were used together with five different atmospheric initial conditions that sample a range of possible initial states of intra-seasonal (stochastic) variability, especially the Madden-Julian Oscillation (MJO), which is considered the primary stochastic variability of relevance to El Nino evolution. The atmospheric initial states were generated from a suite of atmosphere-only integrations forced by observed sea surface temperatures (SST). To the extent that low frequency variability of the tropical atmosphere is forced by slow variations in SST, these atmospheric states should all represent realistic low frequency atmospheric variability that was present in December 1996. However, to the extent that intra-seasonal variability is not constrained by SST, they should capture a range of intra-seasonal states, especially variations in the activity, phase and amplitude of the MJO. For each of these five states, a 20-member ensemble of coupled model forecasts was generated by the addition of small random perturbations to the SST field at the initial time. The ensemble mean from all five sets of forecasts resulted in El Nino but three of the sets produced substantially greater warming by months 4–5 in the NINO3.4 region compared to the other two. The warmer group stemmed from stronger intra-seasonal westerly wind anomalies associated with the MJO that propagated eastward into the central Pacific during the first 1–2 months of the forecast. These were largely absent in the colder group; the weakest of the colder group developed strong easterly wind anomalies, relative to the grand ensemble mean, that propagated into the central Pacific early in the forecast, thereby generating significantly weaker downwelling Kelvin waves in comparison to the warmer group. The strong reduction in downwelling Kelvin waves in the weakest case acted to limit the warming in the eastern Pacific, resulting in a “Modoki” type El Nino that is more focused in the central Pacific. Our results suggest that the intra-seasonal stochastic component of the atmospheric initial condition has an important and potentially predictable impact on the forecasts of the initial warming and flavour of the 1997/1998 El Nino. However, to the extent that atmospheric intra-seasonal variability is not predictable beyond a month or two, these results imply a limit to the accuracy with which the strength and perhaps the spatial distribution of an El Nino can ultimately be predicted. These results do not preclude a predictable role of the MJO and other intra-seasonal stochastic variability at longer lead times if some aspects of the stochastic variability are preconditioned by the evolving state of El Nino or other low frequency boundary forcing.

The Tropical Ocean Global Atmosphere (TOGA) program was a 10-year international climate research effort carried out between 1985 and 1994 under the auspices of the World Climate Research Program (WCRP). TOGA’s goals were to determine the predictability of the coupled ocean-atmosphere system in the tropics on seasonal-to-interannual time scales, to understand the mechanisms responsible for that predictability, and to establish an observing system to support climate prediction. The US contribution to TOGA focused mainly on the El Niño/Southern Oscillation phenomenon, which is the most prominent climate signal on seasonal-to-interannual time scales. One of TOGA’s great strengths was that it forged the three fields of observation, theory, and modeling into a coherent program. TOGA also included climate impact studies from the very beginning by collaborating with scientists outside the field of physical climate research. In this article we highlight some key successes of TOGA and assess its legacy from a perspective of progress over the past 15 years. We also celebrate the fiftieth anniversary of the Intergovernmental Oceanographic Commission (IOC), established within the United Nations Educational, Scientific and Cultural Organization to promote international cooperation in marine research, services, and observations. IOC, together with the World Meteorological Organization and the International Council of Science, cosponsored not only TOGA, but also antecedent and follow-on climate research programs under WCRP. The continuity of these research programs over the time span of decades is one of the reasons for their long-term successes.

The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Niño was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Niño and the Madden–Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Niño. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Niño–type conditions with the amplitude of the warming varying from 0.5° to 2.7°C in the Niño-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996–March 1997 had a significant impact on the subsequent strength of the El Niño event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Niño, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Niño can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propagation into the central Pacific so that forecasts produce sufficient ensemble spread.

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.

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.

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.

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.

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.

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...

The 1997/98 El Nino was one of the strongest on record. Its onset was predicted by several numerical models, though none fully captured its intensity. This was the case for the ECMWF seasonal forecasting system that underestimated the intensification during the June–July 1997 period by more than 1 K. Several strong westerly wind events developed during the onset of the 1997/98 El Nino suggesting that westerly wind events played a key role in the intensification of this El Nino. The present paper quantifies the impact of westerly wind events on the 1997/98 El Nino in the ECMWF seasonal forecasting system, through a series of experiments in which various modifications are made to convective parameterization and wind forcing to increase wind variability in the western Pacific. The ECMWF model does not produce significant westerly wind events. A set of experiments suggests that if the model were able to simulate the May–June westerly wind event, it would have predicted significantly warmer Nino-3 (5°...

Three- and four-dimensional variational assimilation (3DVAR and 4DVAR) systems have been developed for the Ocean Parallelise (OPA) ocean general circulation model of the Laboratoire d'Oceanographie 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-...

Three- and four-dimensional variational assimilation (3DVAR and 4DVAR) systems have been developed for the Ocean Parallelise (OPA) ocean general circulation model (OGCM) of the Laboratoire d'Oceanographie 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 an...

Tropical instability waves (TIWs) appear as monthly oscillations of the currents, sea level, and sea surface temperature of the eastern equatorial Pacific. They are understood as unstable waves feeding on the kinetic and potential energy of the mean currents. A general circulation model is shown to reproduce the main features associated with TIWs. It is then used to investigate the dynamical regime of TIWs, by assessing their sensitivity to oceanic initial conditions. Locally in space and time, small perturbations can grow enough to modify significantly the phase of the TIW field, suggesting some chaotic behavior. When considered over the whole active TIW region, however, the phases of the perturbed and unperturbed experiments remain in agreement. This suggests that TIW activity in this model is more consistent with a limit cycle behavior than with fully developed turbulence and that irregular behavior of TIWs mostly stems from external forcing by the wind. A stronger result is that TIWs in exper...

Intraseasonal Kelvin waves represent an important aspect of tropical ocean dynamics. There has been considerable speculation about whether intraseasonal Kelvin waves generated by intraseasonal wind events can trigger El Nino Southern Oscillation (ENSO) events, and therefore play a potentially important role in the prediction of El Nino. This study considers the different but related issue of how ENSO conditions influence intraseasonal Kelvin waves. Understanding this issue is an important part of clarifying the total role of scale interactions in the ENSO cycle. Numerical experiments carried out with an ocean general circulation model forced with European Centre for Medium-Range Weather Forecasts re-analysis surface fluxes show that both the west-to-east energy transmission and speed of propagation of intraseasonal Kelvin waves are strongly influenced by the phase of ENSO. The waves are much more strongly damped during La Nina conditions than during El Nino, primarily as a result of increased viscous dissipation associated with enhanced vertical shear. We suggest that this mechanism represents a significant source of nonlinearity between El Nino and La Nina states. It may influence the onset, the lifetime and the magnitude of warm or cold anomalies. Copyright © 2002 Royal Meteorological Society

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.

Tropical Instability Waves are a prominent feature in analyses of tropical Pacific sea surface temperature and sea level height variability, and may play a significant role in the heat, salt and momentum budgets of the equatorial oceans. Here we show that they may be strongly influenced by forcing from the atmosphere. In experiments with an ocean GCM we find that a phase shift in the subseasonal wind forcing gives rise to a corresponding phase shift in the TIWs, indicating a systematic influence of the high frequency wind forcing.

In February this year the media was gripped by the emerging disaster in Mozambique. Heavy rains led to devastating floods in the south of the country. Many people lost their lives and many more lost their homes or livelihoods. The country's infrastructure was damaged and the economy suffered. Could these floods have been predicted?

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...

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 Nino–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 s...

Near real time subsurface observations at a PIRATA mooring in the western equatorial Atlantic show a temperature and salinity drop in boreal spring 1999 which is consistent with a 60 m upward shift of the thermocline. At the same time, sea level observations from TOPEX/Poseidon and ERS-1/2 show anomalously low values over a large part of the tropical Atlantic north of the equator. The sea level observations are also used to correct for errors in upper ocean heat content in a global ocean analysis, which is used to initialize seasonal forecasts. It is shown that the ability of altimeter data to reproduce observed temperature changes is limited in the western equatorial Atlantic. This may partly be because large temperature changes occur very rapidly, but is mainly because strong salinity variations compensate up to 10 cm of the sea level signal from temperature.

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.

This paper describes first steps towards the quasi-operational assimilation of sea level anomalies from the TOPEX/POSEIDON and ERS1/2 missions into a global ocean model that is used to initialize a coupled oceanatmosphere seasonal forecast system. We estimate the impact that assimilation of sea level might have on the ocean analysis compared to the assimilation of subsurface temperature observations. As a first step satellite-observed sea level anomalies are compared with simulated sea level anomalies from model experiments. To assess the quality of the ocean analysis and the altimeter data further, model results and satellite data are compared to independent sea level observations from tide gauges. Model results are from three experiments: no assimilation, assimilation of subsurface temperature data, and assimilation of sea level data, respectively. The comparison is focused on the equatorial Pacific where subsurface temperatures are frequently sampled and impact of subsurface oceanic conditions on forecast skill is assumed to be large . It is shown that altimeter data assimilation can match subsurface temperature assimilation in this area.

A hierarchy of El Niño-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models. The statistical models are, in general, based on linear statistical techniques and can be classified into models which use atmospheric (sea level pressure or surface wind) or oceanic (sea surface temperature or a measure of upper ocean heat content) quantities or a combination of oceanic and atmospheric quantities as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the “shallow water” type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast in predicting typical indices of ENSO on lead times of 6 to 12 months. The TOGA program can be regarded as a success from this perspective. However, despite the demonstrated predictability, little is known about ENSO predictability limits and the predictability of phenomena outside the tropical Pacific. Furthermore, the predictability of anomalous features known to be associated with ENSO (e.g., Indian monsoon and Sahel rainfall, southern African drought, and off-equatorial sea surface temperature) needs to be addressed in more detail. As well, the relative importance of different physical mechanisms (in the ocean or atmosphere) has yet to be established. A seasonal dependence in predictability is seen in many models, but the processes responsible for it are not fully understood, and its meaning is still a matter of scientific discussion. Likewise, a marked decadal variation in skill is observed, and the reasons for this are still under investigation. Finally, the different prediction models yield similar skills, although they are initialized quite differently. The reasons for these differences are also unclear.

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 way1, 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 world2. More recently, atmosphere models forced by assumed or predicted ocean conditions have been used3,4. 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 Nino, and include strong signals over Europe.

A four-dimensional variational method is used to examine the extent to which a time sequence of altimeter measurements can determine the subsurface flow in a linear multilayer model of the tropical Pacific Ocean. The experiments are all of the identical-twin type. Complete maps of sea level extracted from the model in a control integration play the role of the altimeter observations in the assimilation experiments. The results of the experiments indicate that, over timescales of months, the sea level information can be effectively propagated into the subsurface, particularly in the dynamically active equatorial region. Several degrees off the equator, however, where waves propagate more slowly, the recovery of the subsurface flow in models containing more than two vertical modes is significantly more difficult. The sensitivity of these results to the lengths of the data sampling and assimilation periods is discussed.

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.

Analyses that largely exploit indirect data from the past 150 years show that El Nino and La Nina might be more predictable than was thought. The results presage the prospect of extended climate forecasts.

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 Nino events. The...

Westward-propagating waves with periods of 20–30 days and wavelengths of ∼ 1,100km are a prominent feature of sea-surface temperatures (SSTs) in the equatorial Pacific and Atlantic Oceans. They have been attributed to instabilities due to current shear. We compare SST observations from the spaceborne Along Track Scanning Radiometer (ATSR) and TOGA-TAO moored buoys with SSTs from a model of the tropical Pacific forced with observed daily windstress data. The phases of the strongest “Tropical Instability Waves” (TIWs) in the model are in closer correspondence with those observed than we would expect if these waves simply developed from infinitesimal disturbances (in which case their phases would be arbitrary). If we filter out the intraseasonal component of the windstress, all phase-correspondence is lost. We conclude that the phases of these waves are not arbitrary, but partially determined by the intraseasonal winds. The subsurface evolution of the model suggests a possible control mechanism is through interaction with remotely-forced subsurface Kelvin and Rossby waves. This is supported by an experiment which shows how zonal wind bursts in the west Pacific can modify the TIW field, but other mechanisms, such as local feedbacks, are also possible.

TOGA was a ten year programme to advance understanding between the atmosphere and ocean which influence climate over interannual time-scales. During its lifetime, a major observational system for the tropical Pacific Ocean was established, with measurements of surface air temperature, surface wind, sea surface temperature, upper ocean thermal structure, ocean currents and sea level being taken on a regular basis, and with trial deployments of instruments to measure surface pressure, rainfall and salinity under way. Armed with these data, oceanographers and meteorologists not only can analyse the present state of the ocean and atmosphere but even can predict climate change for several months ahead. Before TOGA, ideas of tropical atmosphere–ocean interaction were hazy. Ten years on, still no complete theory exists, but considerable progress has been made in modelling and in understanding some aspects of that interaction. In particular the largest climate signal on interannual time-scales, El Nino S...

The effects of zonally varying thermoclines on equatorial, zonally bounded basin modes are investigated analytically. The analytical approach involves a working space transformation and an approximation which drops the terms proportional to the spatial derivative of the thermocline while retaining its spatial variation in the governing equation. This approximation is considered acceptable in the context of equatorially trapped, low-frequency variability. Both free and forced modes were considered. It is found that there exist free normal modes for a given thermocline profile whose frequencies are discrete and depend on both the basin size and shape of the thermocline. This result is in agreement with and complementary to the previous studies. The results show that shoaling thermoclines, relevant to that in the equatorial Pacific, make substantial east-west asymmetry of the upper layer depth anomaly (in a reduced gravity model) over the equator: the amplitude in the east is about twice that in the west. For the forced modes, the amplitude in the east is larger than in the west where the anomalies are generated by imposed wind forcing. As changes in thermocline depth strongly affect sea surface temperature changes in the eastern Pacific, these results provide an estimate of how much the asymmetric wave effects parameterized in some El Nino-Southern Oscillation (ENSO) models can be attributed to the asymmetry of the wave amplitudes owing to the shoaling thermocline.

The predictability of El Nino/Southern Oscillation (ENSO) events is addressed by means of statistical and dynamical schemes. The statistical schemes are based on principal oscillation pattern (POP) analysis of various observed and model ocean fields: these statistical predictions establish a lower limit for the predictability of such a system. For the dynamical predictions, an ocean model of intermediate complexity is coupled to several statistical surface wind stress models. In these coupled models, the atmospheric anomalies are a linear response to the oceanic fields: several combination of fields are considered, such as SST and heat content. The spatial features of predictability are discussed. Predictions seem to be better in the central Pacific. In the western and eastern Pacific, the predictability skill scores are poorer, possibly due to deficiencies in the ocean thermodynamics and in the coupling. The model predictions exhibit a pronounced seasonal dependence, with spring and summer being less predictable. Best results are obtained with seasonally-dependent predictors. DOI: 10.1034/j.1600-0870.1994.00012.x

A coupled model consisting of a reduced gravity ocean and an empirical atmosphere is developed to investigate predictability of ENSO. The ocean model has no seasonal cycle, and is forced only by zonal wind stress anomalies. The empirical atmosphere is constructed by using statistical techniques to relate observed wind stress anomalies directly to the sea surface temperature anomalies simulated by the model. The focus of this study is on detailed analysis of individual predictions, as well as on statistical scores from an ensemble of predictions. The model can in general successfully predict an event up to about one year in advance, but the model has little ability to predict low amplitude variability that is not related to ENSO. Predictions based on POP analysis of the ocean model data are also made for several ENSO events, with skill comparable to that of the dynamical coupled model. DOI: 10.1034/j.1600-0870.1994.00010.x

Many features of the El Nino–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 ...

The authors aim to acquaint the reader with the current state of ocean circulation models, their ability to model the present climate state and its variability, and their major shortcomings and uncertainties. They limit the discussion to three-dimensional models of the physical system. They begin by describing the basic structure of circulation models, and discussing various problems with their implementation. They give a brief overview of the types of observational data in oceanography, and the ways in which the data are used. Some results from models of the wind-driven circulation are discussed, with particular emphasis on the dynamics of mesoscale eddies. Considerable progress has been made in understanding short-term variability associated with ENSO, and the authors describe ocean-atmosphere interactions in the tropics as well as results from coupled ocean-atmosphere models for ENSO variability. Models of the thermohaline circulation are described and some emerging ideas regarding long-term changes are given.

[1] Seasonal forecasting is a rapidly developing field with considerable effort devoted to developing comprehensive coupled general circulation models. Initialising these models is also important, as the forecast skill depends strongly on the way the coupled model is initialised. Three commonly used strategies are evaluated using the ECMWF System3, a state of the art coupled model. The most skillful scheme is that which makes most use of atmospheric and oceanic data even though this may generate initial imbalances in the coupled state. At seasonal time scales, increasing the balance at the cost of being further from the observations can degrade the forecasts. The relative importance of different components of the ocean observing system, equatorial moorings, altimetery and Argo floats, on forecast skill is assessed.

The latest version of the ECMWF ocean analysis system was recently introduced into operational use. This not only provides initial conditions for the monthly and seasonal forecast systems but also creates a historical reanalysis. For the first time, altimeter data are used in the ECMWF operational ocean analysis. However, making good use of altimetric sea level information was not easy and several difficulties had to be overcome. Various strategies were tried and compared. Attempts to use mean sea level from gravimetric satellites were also tried, but no satisfactory method of using this data was found. The altimetric data used show a marked rising trend that cannot be directly represented in the model as the Boussinesq approximation is used. A strategy for dealing with the trend is given. Results of Observing System Experiments (OSEs) with and without altimeter data are described and results are compared to illustrate the benefits of using altimetry. Although the results are positive, further developments are needed to more fully utilize the data.

The best documented example of natural climate variability occurs mainly in the tropical Pacific every few years. It involves oceanic and atmospheric interactions, and although centred in the Pacific, influences climate over extensive areas of the globe ranging from the Asian monsoon and the Sahel in the west to Equador and Peru in the east. In the past few years model experiments have been performed to elucidate its origins and to predict its occurrence. It is hoped that the experience from these model experiments can be extended to other climate variations such as might result from increasing concentrations of radiatively active gases.

The enthusiasm for engaging the challenges of Seasonal to Interannual Prediction, both within the disciplines of physical and social sciences and at their interface, was well demonstrated through the energetic engagement of all during the May to June 2005 NATO ASI course, upon which this book is based. Several panel sessions were held during the course, which permitted everyone to offer views within an informal setting; some, not reflected in the main body of the book, are incorporated in this chapter. Little stays stationary in such a fast-developing field, and so, to provide the most advanced position at publication, this summarising chapter has included some of the latest development to supplement the material drawn from the course presentations and the panel discussions. Additionally, a view to the future is offered so as to provide further stimulation to those interested in the fascinating field of Seasonal to Interannual Climate.