Técnicas de comprensión lectora

As it turns out, and despite our general understanding of local and remote processes, our current ability to forecast tropical Atlantic SST anomalies is limited. This is partly due to the

Section 3), and partly due to the fact that the dynamics governing SST variability is complex and poorly understood. A third source of difficulty is the inability of the present generation of coupled global models to correctly simulate the underlying climate of the tropical Atlantic region

(Davey et al. 2002).

An analysis of the effect of SST specification errors on the errors in rainfall prediction within the tropical Atlantic region in the context of a two-tiered prediction system is provided by Goddard and Mason (2002). The key figure from this study (Figure 4.5) shows the boreal spring and summer rainfall error pattern and the associated SST specification error resulting from assuming persistence of SST in a seasonal forecast with one season time lead. The errors in predicted spring rainfall are largest over northeastern Brazil and are linked with an error in specifying the SST gradient across the equator. In the summer, the precipitation forecast errors

are largest over West Africa and are linked with an error in specifying SST along the equator

.

Figure 4.5: The dominant patterns of precipitation errors (over land) and their associated pattern of SST anomaly error for March-May (left) and June-August in a three month lead prediction with an atmospheric GCM forced with specified SST derived under the assumption of persistence. The experiment was performed for the years 1970–1996. The SST error is defined as the observed MAM (or JJA) SST – persisted February (or May) SST; the precipitation error is defined as the simulated precipitation – precipitation calculated with observed SST. Red/blue are for positive/negative SST error and green/yellow are for positive/negative precipitation error. Contour interval is 0.1 for both SST and precipitation patterns in relative (normalized) units (from Goddard and Mason, 2002).

There is a clear link between the error patterns in Figure 4.5 and the patterns of TAV described in the previous section. This is not surprising. The errors in rainfall are due to errors in correctly predicting the underlying dominant patterns of SST variability, which can be viewed as forcing the location and intensity of the marine ITCZ and its influences over the African and South American seaboards. The obstacle to prediction of rainfall in the tropical Atlantic can thus be linked to our ability to predict SST in the basin, as also supported by Figure 4.1. This however is a somewhat simplified view of regional processes that display evidence for coupled behavior in the form of thermodynamical and dynamical positive feedbacks. Such are the WES feedbacks in the western equatorial Atlantic during the boreal spring and the plausible Bjerknes feedback in the east during the summer. The presence of such feedbacks calls for the use of coupled models for prediction.

The thermodynamic feedback and the remote influence of El Niño are the two dominant factors affecting the predictability of TAV during the boreal winter and early spring – the time of year associated with the onset of the meridional mode. To first order, these processes can be captured with an atmospheric GCM coupled to a slab ocean and forced with Pacific SST anomalies. Missing in this approach is a way to incorporate the largely unpredictable effect of

the Atlantic extratropics, north (e.g., NAO) and south of the equator. Chang et al. (2004)

adopted such a model strategy. They conducted two sets of seasonal forecast experiments. In the first the mixed layer ocean was initialized with observed December SST everywhere in the global ocean (hereafter Global Initial Condition or GIC experiment) and the coupled model was integrated forward for 9 months. The experiment was conducted year-by-year over the 42-year

interval 1959 to 2000 repeating each year’s “hindcast” ten times, each starting with a slightly different initial December atmospheric state taken from the NCEP/NCAR reanalysis. The second set of experiments was identical to the first except that the observed SSTs used to initialize the mixed layer ocean were limited to the Atlantic Ocean only, between 30°S and 60°N (hereafter Atlantic Initial Condition or AIC experiment). In the latter set of runs, the SST anomalies were set to zero everywhere outside the Atlantic domain. The purpose of the second set of experiments was to examine the extent to which the predictability can be captured by the thermodynamic feedback between the atmosphere and the mixed layer ocean within the Atlantic basin in the absence of external forcing (El Niño in particular).

When validating the ensemble mean predictions against the observed SST, Chang et al. found

that the coupled model has considerable skill in forecasting the SST anomaly in the tropical Atlantic during boreal spring. Off-equatorial SST anomalies, particularly those in the tropical north Atlantic, can be predicted two seasons in advance by the coupled model with a high degree of skill. Even in the absence of the remote El Niño influence (AIC experiment), the model skill is considerably superior to the skill of assuming the persistence of SST anomalies in that region.

Saravanan and Chang (2004) took a closer look at the role of thermodynamic air-sea feedback. They examined two thermodynamic feedback mechanisms: the reduced thermal damping mechanism (e.g., Barsugli and Battisti (2000)) and the WES feedback. The results show that thermodynamic coupling leads to amplification and increased persistence of surface wind variability in the deep tropical Atlantic. This effect is anisotropic, being stronger in the meridional component than in the zonal component of the surface wind. Since these features cannot be explained by the isotropic reduced thermal damping mechanism, it suggests that the WES feedback plays in an important role in enhancing the model’s prediction skill, contributing to forecasts of north tropical Atlantic SST that are significantly better than persistence forecasts during the boreal spring.