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S. Ricci, A. T. Weaver, J. Vialard, and P. Rogel

Abstract

Several studies have illustrated how the univariate assimilation of temperature data can have a detrimental effect on the ocean-state variables (salinity, currents, etc.) not directly constrained by the data. In this paper, the authors describe how the salinity adjustment method proposed by Troccoli and Haines can be included as a multivariate temperature–salinity (TS) constraint within a background-error covariance model for variational data assimilation. The method is applied to a three-dimensional variational assimilation (3DVAR) system for a tropical Pacific version of the Océan Parallélisé (OPA) ocean general circulation model. An identical twin experiment is presented first to illustrate how the method is effective in reconstructing a density profile using only temperature observations from that profile. The 3DVAR system is then cycled over the period 1993–98 using in situ temperature data from the Global Temperature and Salinity Pilot Programme. Relative to a univariate (T) 3DVAR, the multivariate (T, S) 3DVAR significantly improves the salinity mean state. A comparison with salinity data that are not assimilated is also presented. The fit to these observations is improved when the TS constraint is applied. The salinity correction leads to a better preservation of the salinity structure and avoids the development of spurious geostrophic currents that were evident in the univariate analysis. The currents at the surface and below the core of the undercurrent are also improved.

Examination of the heat budget highlights how the temperature increment must compensate for a perpetual degradation of the temperature field by abnormally strong advection in the univariate experiment. When the TS constraint is applied, this spurious advection is reduced and the mean temperature increment is decreased. Examination of the salt budget shows that spurious advection is also the main cause of the upper-ocean freshening. When the TS constraint is applied, the salinity structure is improved allowing for a better representation of the advection term and better preservation of the salt content in the upper ocean. The TS constraint does not correct for all problems linked to data assimilation: vertical mixing is still too strong, and the surface salinity state and currents still have substantial errors. Improvements can be expected by including additional constraints in the background error covariances and by assimilating salinity data.

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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.

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J. P. Duvel, C. Basdevant, H. Bellenger, G. Reverdin, A. Vargas, and J. Vialard

During the Validation of the Aeroclipper System under Convective Occurrences (VASCO) test experiment in January and February 2007, eight Aeroclipper prototypes were launched from Mahe Island in the tropical Indian Ocean. The Aeroclipper is a streamlined balloon maintained in the atmospheric surface layer by a guide rope dragging on the ocean surface. While requiring some design improvements, these prototypes showed good potential for the exploration of the tropical air-sea interface, even under rough cyclonic conditions.

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S. Neetu, M. Lengaigne, J. Vialard, M. Mangeas, C.E. Menkes, I. Suresh, J. Leloup, and J.A. Knaff

Abstract

While tropical cyclone (TC) track forecasts have become increasingly accurate over recent decades, intensity forecasts from both numerical models and statistical schemes have been trailing behind. Most operational statistical–dynamical forecasts of TC intensity use linear regression to relate the initial TC characteristics and most relevant large-scale environmental parameters along the TC track to the TC intensification rate. Yet, many physical processes involved in TC intensification are nonlinear, hence potentially hindering the skill of those linear schemes. Here, we develop two nonlinear TC intensity hindcast schemes, for the first time globally. These schemes are based on either support vector machine (SVM) or artificial neural network (ANN) algorithms. Contrary to linear schemes, which perform slightly better when trained individually over each TC basin, nonlinear methods perform best when trained globally. Globally trained nonlinear schemes improve TC intensity hindcasts relative to regionally trained linear schemes in all TC-prone basins, especially the SVM scheme for which this improvement reaches ~10% globally. The SVM scheme, in particular, partially corrects the tendency of the linear scheme to underperform for moderate intensity (category 2 and less on the Saffir–Simpson scale) and decaying TCs. Although the TC intensity hindcast skill improvements described above are an upper limit of what could be achieved operationally (when using forecasted TC tracks and environmental parameters), it is comparable to that achieved by operational forecasts over the last 20 years. This improvement is sufficiently large to motivate more testing of nonlinear methods for statistical TC intensity prediction at operational centers.

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Jérôme Vialard, Christophe Menkes, Jean-Philippe Boulanger, Pascale Delecluse, Eric Guilyardi, Michael J. McPhaden, and Gurvan Madec

Abstract

In this study, the processes affecting sea surface temperature variability over the 1992–98 period, encompassing the very strong 1997–98 El Niño event, are analyzed. A tropical Pacific Ocean general circulation model, forced by a combination of weekly ERS1–2 and TAO wind stresses, and climatological heat and freshwater fluxes, is first validated against observations. The model reproduces the main features of the tropical Pacific mean state, despite a weaker than observed thermal stratification, a 0.1 m s−1 too strong (weak) South Equatorial Current (North Equatorial Countercurrent), and a slight underestimate of the Equatorial Undercurrent. Good agreement is found between the model dynamic height and TOPEX/Poseidon sea level variability, with correlation/rms differences of 0.80/4.7 cm on average in the 10°N–10°S band. The model sea surface temperature variability is a bit weak, but reproduces the main features of interannual variability during the 1992–98 period. The model compares well with the TAO current variability at the equator, with correlation/rms differences of 0.81/0.23 m s−1 for surface currents. The model therefore reproduces well the observed interannual variability, with wind stress as the only interannually varying forcing.

This good agreement with observations provides confidence in the comprehensive three-dimensional circulation and thermal structure of the model. A close examination of mixed layer heat balance is thus undertaken, contrasting the mean seasonal cycle of the 1993–96 period and the 1997–98 El Niño. In the eastern Pacific, cooling by exchanges with the subsurface (vertical advection, mixing, and entrainment), the atmospheric forcing, and the eddies (mainly the tropical instability waves) are the three main contributors to the heat budget. In the central–western Pacific, the zonal advection by low-frequency currents becomes the main contributor. Westerly wind bursts (in December 1996 and March and June 1997) were found to play a decisive role in the onset of the 1997–98 El Niño. They contributed to the early warming in the eastern Pacific because the downwelling Kelvin waves that they excited diminished subsurface cooling there. But it is mainly through eastward advection of the warm pool that they generated temperature anomalies in the central Pacific. The end of El Niño can be linked to the large-scale easterly anomalies that developed in the western Pacific and spread eastward, from the end of 1997 onward. In the far-western Pacific, because of the shallower than normal thermocline, these easterlies cooled the SST by vertical processes. In the central Pacific, easterlies pushed the warm pool back to the west. In the east, they led to a shallower thermocline, which ultimately allowed subsurface cooling to resume and to quickly cool the surface layer.

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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.

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Gregory R. Foltz, Jérôme Vialard, B. Praveen Kumar, and Michael J. McPhaden

Abstract

Sea surface temperature (SST) in the southwestern tropical Indian Ocean exerts a significant influence on global climate through its influence on the Indian summer monsoon and Northern Hemisphere atmospheric circulation. In this study, measurements from a long-term moored buoy are used in conjunction with satellite, in situ, and atmospheric reanalysis datasets to analyze the seasonal mixed layer heat balance in the thermocline ridge region of the southwestern tropical Indian Ocean. This region is characterized by a shallow mean thermocline (90 m, as measured by the 20°C isotherm) and pronounced seasonal cycles of Ekman pumping and SST (seasonal ranges of −0.1 to 0.6 m day−1 and 26°–29.5°C, respectively). It is found that surface heat fluxes and horizontal heat advection contribute significantly to the seasonal cycle of mixed layer heat storage. The net surface heat flux tends to warm the mixed layer throughout the year and is strongest during boreal fall and winter, when surface shortwave radiation is highest and latent heat loss is weakest. Horizontal heat advection provides warming during boreal summer and fall, when southwestward surface currents and horizontal SST gradients are strongest, and is close to zero during the remainder of the year. Vertical turbulent mixing, estimated as a residual in the heat balance, also undergoes a significant seasonal cycle. Cooling from this term is strongest in boreal summer, when surface wind and buoyancy forcing are strongest, the thermocline ridge is shallow (<90 m), and the mixed layer is deepening. These empirical results provide a framework for addressing intraseasonal and interannual climate variations, which are dynamically linked to the seasonal cycle, in the southwestern tropical Indian Ocean. They also provide a quantitative basis for assessing the accuracy of numerical ocean model simulations in the region.

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J. Segschneider, D. L. T. Anderson, J. Vialard, M. Balmaseda, T. N. Stockdale, A. Troccoli, and K. Haines

Abstract

In this paper, the combined assimilation of satellite observed sea level anomalies and in situ temperature data into a global ocean model, which is used to initialize a coupled ocean–atmosphere forecast system, is described. The altimeter data are first used to create synthetic temperature observations, which are then combined with the directly observed temperature profiles in an optimum interpolation scheme. In addition to temperature, salinity is corrected based on a preservation of the model's local temperature–salinity relationship. Coupled forecasts with a lead time of up to 6 months are initialized from the ocean analyses and the impact of the data assimilation on both the ocean analysis and the coupled forecasts is investigated. It is shown that forecasts of sea surface temperature anomalies in the Niño-3 area can be improved by initializing the coupled forecast model with the ocean analysis in which temperature and altimeter data are assimilated in combination. The results further imply that a good simulation of the salinity field is required to make optimum use of the altimeter data.

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J. Zavala-Garay, C. Zhang, A. M. Moore, A. T. Wittenberg, M. J. Harrison, A. Rosati, Jérôme Vialard, and R. Kleeman

Abstract

A common practice in the design of forecast models for ENSO is to couple ocean general circulation models to simple atmospheric models. Therefore, by construction these models (known as hybrid ENSO models) do not resolve various kinds of atmospheric variability [e.g., the Madden–Julian oscillation (MJO) and westerly wind bursts] that are often regarded as “unwanted noise.” In this work the sensitivity of three hybrid ENSO models to this unresolved atmospheric variability is studied. The hybrid coupled models were tuned to be asymptotically stable and the magnitude, and spatial and temporal structure of the unresolved variability was extracted from observations. The results suggest that this neglected variability can add an important piece of realism and forecast skill to the hybrid models. The models were found to respond linearly to the low-frequency part of the neglected atmospheric variability, in agreement with previous findings with intermediate models. While the wind anomalies associated with the MJO typically explain a small fraction of the unresolved variability, a large fraction of the interannual variability can be excited by this forcing. A large correlation was found between interannual anomalies of Kelvin waves forced by the intraseasonal MJO and the Kelvin waves forced by the low-frequency part of the MJO. That is, in years when the MJO tends to be more active it also produces a larger low-frequency contribution, which can then resonate with the large-scale coupled system. Other kinds of atmospheric variability not related to the MJO can also produce interannual anomalies in the hybrid models. However, when projected on the characteristics of Kelvin waves, no clear correlation between its low-frequency content and its intraseasonal activity was found. This suggests that understanding the mechanisms by which the intraseasonal MJO interacts with the ocean to modulate its low-frequency content may help to better to predict ENSO variability.

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Weiqing Han, Jérôme Vialard, Michael J. McPhaden, Tong Lee, Yukio Masumoto, Ming Feng, and Will P.M. de Ruijter

The international scientific community has highlighted decadal and multidecadal climate variability as a priority area for climate research. The Indian Ocean rim region is home to one-third of the world's population, mostly living in developing countries that are vulnerable to climate variability and to the increasing pressure of anthropogenic climate change. Yet, while prominent decadal and multidecadal variations occur in the Indian Ocean, they have been less studied than those in the Pacific and Atlantic Oceans. This paper reviews existing literature on these Indian Ocean variations, including observational evidence, physical mechanisms, and climatic impacts. This paper also identifies major issues and challenges for future Indian Ocean research on decadal and multidecadal variability.

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