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## Gayana (Concepción)

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*versión impresa* ISSN 0717-652X*versión On-line* ISSN 0717-6538

### Gayana (Concepc.) v.68 n.2 supl.TIProc Concepción 2004

#### http://dx.doi.org/10.4067/S0717-65382004000200027

Gayana 68(2) supl. t.I. Proc. : 144-150, 2004 ISSN 0717-652X
1. LEGOS/IRD/CNES, 14 av. Edouard Belin, 31401 Toulouse Cedex, France
An intermediate ocean-atmosphere coupled model of the tropical Pacific is used to investigate the sensitivity of the seasonal forecasts to the configuration of the oceanic vertical structure. The models consist in a three baroclinic mode tropical Pacific Ocean model and a Gill (1980)'s tropical atmosphere. The predictive skill of the model using a simple nudging method for the initialization is first presented from 1970 and compared to the results of other prediction system of similar complexity, which emphasizes the modulation of the skill on decadal timescales. It is then demonstrated that the skill is critically dependant on the energy distribution on the baroclinic mode, higher-order mode contributions being favored at some period and not at others. Linear Green's function are used to assimilate satellite observations (SST and wind) and derive the optimized set of parameters that determines the vertical structure in the model for a particular period of time. The scheme is first tested for the period prior to the development of the 1997 El Niño. It is shown that substantial improvement in forecasting the event is realized for an increase of the relative contribution of the higher-order modes through the model parameters setting. Assimilation experiments of satellite data for the September 2003-February 2004 period are carried for producing initial conditions for the coupled model. Results of forecasts runs for 2004 are presented and discussed.
Despite the fact that the tropical Pacific has never been so well observed, the last 1997/1998 El Niño was surprisingly the most unexpected event of the last 15 years. In particular, the Lamont-Doherty Earth Observatory model (hereinafter referred to as the Lamont model) which used to be skillful in the 80s and early 90s (Cane et al., 1986; see the Climate Diagnostic Bulletins), failed to predict the onset of this strong warm event. Similarly, the 2002 El Niño, although of much weaker amplitude than the 1997/98 event was not well detected with this simple prediction system. Several explanations were offered. First, the Lamont model is very sensitive to the initialization: Chen et al. (1999) noted a large sensitivity of the predictions to the wind products for the 1997/1998 El Niño. Initializing the prediction runs with sea level anomalies derived from observations also results in a large range of behaviors of this model (Périgaud et al. 2000). Second, as an anomaly model with prescribed mean states, it can not grasp climate changes which are likely to impact El Niño occurrence. As the general circulation models (GCMs) with extensive assimilation schemes have done a better job in recent year, this calls for revising the simple prediction systems by including more sophisticated assimilation schemes (Chen et al. 1995, hereafter CZBC95) and/or including more physics. Dewitte (2000) (hereafter D0) has included more vertical modes into the oceanic component of a model similar to Lamont in order to better represent the thermocline depth fluctuations and the dissipation of the waves associated to vertical propagation of energy. This was motivated by recent observational and modeling studies which indicate that more than one baroclinic mode is necessary to capture correctly the sea level and zonal current variability (McPhaden, 1999). Sensitivity experiments with this model to the energy distribution on the baroclinic mode have shown that the predictions were sensitive to the subsurface condition prior to the development of the ENSO event (Dewitte et al., 2002). In particular, the model better grasp the rise in temperature and amplitude at the mature phase of the 1997/98 El Niño. Their results however still leads much space for improvement of the predictions, which can be obtained from data assimilation. In the absence of realistic estimate of the baroclinic contribution to, say, sea level or current anomalies from observations, there is a need to develop assimilation techniques that take into account the specificity of the baroclinic modes. In this paper, we present an assimilation method that allows projecting surface observations on the model baroclinic modes contribution. We show that it provides initial conditions for the model that lead to improvement in forecasting compared to the nudging method. The results of Dewitte et al (2002) are confirmed, namely that the contribution of higher-order baroclinic mode are important for ENSO predictions and that the second baroclinic mode contribution was increased prior to the development for the 1997/98 El Niño, which has participated to a large extent to its intensity. The study also provides an estimate of the variability that projects on the first 3 baroclinic modes prior to the development of the 1997/1998 El Niño event and analyse prediction for 2004. The paper is organised as follows. Section 2 is devoted to the data, model and assimilation method description. Section 3 presents the results and section 4 is a discussion.
We use a tropical Pacific ocean-atmosphere model of intermediate complexity. It is an extension of the Zebiak and Cane (1987)'s model (hereafter ZC model) in that it is based on similar physics, i.e. shallow-water for both components. The ocean component includes 3 baroclinic modes with characteristic phase speed c
Like for SST, the anomalies are relative to the seasonal cycle computed over the periods, January 1982-December 2001 for the predictions of 1997/98, whereas they are obtained by removing the climatological seasonal cycle averaged between January 1992 to December 2003 for predicting the 2004 conditions. The data were monthly averaged and interpolated on a 2
The method of linear Green's Functions (Menemelis and Wunsh, 1996) is used to improve ICM initial conditions and parameters with data assimilation. A linearity hypothesis is done by assuming that large-scale differences between the model's predictions and the data on a reduced grid are sufficiently small for their evolution to be linear. After a perturbation experiment to test the model's stability and prepare the linearization, the Green's Functions are calculated to build a linear model which describes the evolution of these differences. Then, we use the Gauss-Markov's estimator to minimise a cost function and obtained the best fit for the desired parameters. The formalism is detailed in the Appendix. As mentioned above, only SST and zonal wind stress anomalies were assimilated. Two critical parameters of ICM were chosen to be optimised: The projection coefficient, P _{n}. Using the assimilation method proposed here, it is interesting to evaluate if assimilation of data in ICM leads to changes in P_{n} similar to what was guessed empirically in Dewitte et al. (2002). We also complement the latter study by providing estimate of change in scl_{n}.
Several assimilation windows, never exceeding 6 months (due to computational constrain), were chosen in order to assess from which date and duration, the assimilation of data is useful in improving the forecasts compared to the classical nudging procedure (CZBC95). Each times, changes in (P
The NINO3 SST index (SST anomalies average over (150°W-90°W; 5°S-5°N)) are displayed in figure 1 for one experiment (assimilation window: Nov-Apr 1996/1997). The assimilation of data results in an improved prediction of the 1997/98 El Niño, with a more realistic amplitude of SSTA at the mature phase of the event as long as with a better timing of the peak. The growth phase of the event is also better simulated with less delay between simulation and observation at the early stage of the development of the event. Similar improvements of the prediction are obtained for the assimilation windows of 6 and 4 months spanning December to May 1996/1997, January to June 1997 and March to June 1997. For the experiment with a 2 months windows, May to June 1997, the system is as skilful as the standard predictions with the peak of the event underestimated by ~30% as compared to the observations (not shown). It is now interesting to analyse the impact of the assimilation on the (P Figure 1 : NINO3SST index for the observations (dash-doted line), for the model using the standard initialisation method (thick dashed line) (initial conditions in May 1997) and for the model with the data assimilation procedure starting in November 1996 until April 1997 (middle solid thin line). Prediction with assimilation is the average of one hundred perturbed predictions runs, standard deviation curves are also represented (thin solid lines). Consistently with Dewitte et al. (2002), the experiments that lead to improve simulation of the peak phase of the 1997/98 El Niño event are associated with an increased relative contribution of the second baroclinic mode. For instance, for Exp1 (figure 1), the increase in P
Similar experiments were performed for predicting SSTAs for the second half of 2004. The results indicate a much larger sensitivity of the results to the period over which the assimilation is performed. In all the runs, the model predicts slightly warm conditions (not shown). However, the impact on the (P
The sensitivity to the relative contribution of the baroclinic modes in a intermediate prediction system is confirmed using an assimilation method. It uses SST and zonal wind stress satellite derived data to optimise the initial conditions corresponding to SSTAs and the baroclinic mode parameters (P Further sensitivity tests are carried out for year 2004. The system predicts slightly warm condition developing in the second half of 2004. However, the range of deviations of (P
A predictive model can be writing as follows: ξ(t + 1) = ψ(ξ( where B The linearity hypothesis is done by assuming that large-scale differences between the model's predictions and the ocean's real state on reduced grid are sufficiently small for their evolution to be linear. Thus, its evolution is governed by an equation whom form is: A perturbation experiment is done to test the model stability and prepare the linearization. Model Green's function (Wunsch, 1996), is defined here as the linear model response in SST and zonal component of wind stress to either, unit temperature perturbations, unit perturbations or unit x(t). To obtain a sensible linear model, the transition matrix has to be dependent on time and is calculated through the Green Function _{o}G(t,_{1}t). _{2}The perturbations are introduced on each element of the reduced grid and projected onto the original grid usingB. The full predictive model run and the response in SST and zonal component of wind stress are projected back onto the reduced grid. The resulting vector gives the column of the Green Function corresponding to each perturbed element. From any initial state x(t+_{o} t) = G(t+_{o} t, t)_{o}x(t). Assimilation is done on this reduced grid, minimizing the deviation between the ICM prediction and the data. _{o}The data η( The total inversion method is to consider whole the data and using Gauss-Markov's estimation to determinate the optimum initial state. In our experiments, the initial state corresponds to SST, projection and 'thermocline' coefficients (P scl). At any time, measurement equations take on the form: _{n}
That can be put on under matrix form (Stammer and Wunsch, 1996): This equation can be rewritten: Gauss-Markov's estimation (Wunsch, 1996), through a statistical estimation theory, allows to find an estimate and its corresponding (as well as their uncertainties P These expressions form the estimator that permits to find the optimum initial state δscl were included in the _{n}x(0) vector.REFERENCES Bentamy, A., Y. Quilfen, F. Gohin, N. Grima, M. Lenaour, & J. Servain, Determination and validation of average wind fields from ERS-1 scatterometer measurements. Chen, D., S. E. Zebiak, A. J. Busalacchi, & M. A. Cane, An improved procedure for El Niño forecasting: Implication for predictability. Science, 269, 1699-1702, 1995. [ 2] Dewitte B. & C. Périgaud, 1996 : El Niño-La Niña events simulated with the Cane and Zebiak's model and observed with satellite or in situ data. Part I: Model forced with observations. Dewitte B., 2000: Sensitivity of an intermediate coupled ocean-atmosphere model of the tropical Pacific to its oceanic vertical structure. Dewitte B., D. Gushchina, Y. duPenhoat and S. Lakeev, 2002: On the importance of subsurface variability for ENSO simulation and prediction with intermediate coupled models of the Tropical Pacific: A case study for the 1997-1998 El Niño. Dewitte B., S. Illig, L. Parent, Y. duPenhoat, L. Gourdeau & J. Verron, 2003: Tropical Pacific baroclinic mode contribution and associated long waves for the 1994-1999 period from an assimilation experiment with altimetric data. Gill, A., 1980: Some simple solutions heat-induced tropical circulation. Menemenlis, D., & C. Wunsh, 1996: Linearization of an Oceanic General Circulation Model for Data Assimilation and Climate Studies. Menkes C. & coauthors (1998) Impact of TAO vs. ERS wind stresses onto simulations of the tropical Pacific Ocean during the 1993-1998 period by the OPA OGCM. Climate Impact of Scale Interactions for the Tropical Ocean-Atmosphere System, Euroclivar Workshop Report, Eucliv 13, pp. 46-48. [ 9] McPhaden M., Genesis & evolution of the 1997-98 El Niño, Science Wunsh, C., 1996: The Ocean Circulation Inverse Problem. Zebiak, S. E., & M. A. Cane, 1987: A model El Niño-Southern Oscillation. |