**5. Conclusion**

134 Remote Sensing of Biomass – Principles and Applications

Fig. 8. First spatial patterns of (a) SLA and (b) CHL anomalies at intra-annual time scales. The patterns are scaled to represent the amplitude of SLA and CHL anomalies associated

The spatial pattern for the first mode between the SLA and CHL intra-annual anomalies shows a significant positive covariation of SLA and CHL in the whole Mediterranean Sea. Note that variations in CHL at this time scale are markedly lower than in other modes. This behavior does not agree with that observed at inter-annual time scales, when the winter mixing enhances the upward transport of nutrients to the ocean surface. Therefore, other physical processes must be regarded in order to explain the biomass enhancement at this time scale. One candidate could be a mechanical effect indebted to oscillations in the thermocline. The modification of the thermocline depth due to the SLA variation could accumulate phytoplankton biomass close to the surface (or vice versa) and thus affect the CHL measured by the ocean color satellites without modifying the vertically-integrated

with 1 standard deviation of the first expansion coefficients.

**4.2 Intra-annual anomalies** 

This study analyzes the basin scale physical-biological coupling in the Mediterranean Sea at inter-annual, seasonal and intra-annual time scales based on 12 years of concurrent satellite SLA and CHL data. Not surprisingly, the long-term (inter-annual and seasonal) variability of SLA and CHL is negatively correlated in most oceanic areas of the Mediterranean Sea. This is the typical behavior of temperate regions associated with the availability of nutrients in the mixed layer: summer stratification blocks upward entrainment of nutrients from deep layers and winter mixing brings nutrients to the surface (Cushing, 1959). In the coastal regions, particularly in those areas influenced by major rivers, the biological response is controlled by supply of nutrients of continental origin. However, other biological responses

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to the thermocline oscillations are also observed. The inter-annual SLA and CHL covary in areas dominated by mesoscale eddies, such as the Levantine basin and the southern part of the Western basin. Cyclonic eddies can enhance primary production by upwelling of nutrient rich water (McGillicuddy et al., 1998; Oschlies & Garçon, 1998). In the intra-annual variability, the coupling between SLA and CHL is exerted through a mechanical compression mechanism, which concentrates nutrients and phytoplankton cells into the surface layer. Nevertheless, the overall influence of the mesoscale eddies and the compression mechanism in the enhancement of phytoplankton in the Mediterranean Sea deserves further study.

The SVD analysis to link SLA and CHL is a quick, easily accessible and powerful method for assessing the ocean physical-biological coupling. Our results demonstrate its strength over the direct correlation. The correlation map indicates the spatial covariability of SLA and CHL but cannot provide any details about their temporal variability. SVD analysis extracts the dominant temporal and spatial components of covariability between SLA and CHL into a series of orthogonal functions or statistical modes, and their time evolution or expansion coefficients. In addition, the SVD modes can be related to different coupling mechanism. This methodology represents a simple alternative to more sophisticated coupled physicalbiological ocean models. There are also other conceptual methods that isolate coupled modes of variability between spatial time series, such as joint EOFs or canonical correlation analysis. According to Bretherton et al. (1992), the SVD analysis is simpler and superior than these other methods in most situations involving geophysical fields.
