Abstract

Abstract. Differences, similarities and complementarities between Sea Level Anomalies (SLA) deduced from altimeter measurements and dynamic height anomalies (DHA) calculated from Argo in situ temperature (T) and salinity (S) profiles are globally analyzed. SLA and DHA agree remarkably well and, compared to previous studies, Argo dataset allows an improvement in the coherence between SLA and DHA. Indeed, Argo data provides a much better spatial coverage of all oceans and particularly the Southern Ocean, the use of an Argo mean dynamic height, the use of measured salinity profiles (versus climatological salinity), and the use of a deeper reference level (1000 m versus 700 m). The large influence of Argo salinity observations on the consistency between altimetry and hydrographic observations is particularly demonstrated with an improvement of 35% (relative to the SLA minus DHA signal) by using measured salinity profiles instead of climatological data. The availability of observations along the Argo float trajectories also provides a means to describe the sea level variability of the global ocean both for the low frequency and the mesoscale part of the circulation. Results indicate that sea level variability is dominated by baroclinic signal at seasonal to inter-annual periods for all latitudes. In the tropics, sea level variability is baroclinic for meso-scale to interannual periods and at high latitudes, sea level variability is barotropic with also deep baroclinic signals (i.e. influence of deep temperature and salinity signals) for intra seasonal and mesoscale periods. These results emphasize the need to separate the different time and space scales in order to improve the merging of the two data sets. The qualitative study of seasonal to interannual SLA minus DHA signals finally reveals signals related to deep ocean circulation variations and basin-scale barotropic signals. Future work is, however, needed to understand the observed differences and relate them to different forcing mechanisms.

Highlights

  • In November 2007, the global Argo array of profiling floats reached its initial target of 3000 operating floats worldwide

  • In GLL06, authors showed very good correlation between the two data sets and systematic large-scale differences that have been related to a barotropic, Sverdrup-like, response of the ocean to wind forcing

  • The Argo data set allows a series of improvements ranging from: (1) a better global spatial coverage (i.e. Southern Ocean), (2) a deeper reference level (1000 m versus 700 m), (3) the use of measured S profiles, (4) the separation of the different temporal scales and (5) the study of seasonal to interannual Sea Level Anomalies (SLA) minus dynamic height anomalies (DHA) signals

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Summary

Introduction

In November 2007, the global Argo array of profiling floats reached its initial target of 3000 operating floats worldwide. Satellite altimeters continue to provide global and synoptic measurements of sea level variations These observations are the two most important and complementary components of the global ocean observing system required by climate and operational oceanography applications (Bell et al, 2009). In GLL06, authors showed very good correlation between the two data sets and systematic large-scale differences that have been related to a barotropic, Sverdrup-like, response of the ocean to wind forcing This previous study, focused on the 1993–2003 period, was limited by the in situ observations available. The Argo data set allows a series of improvements ranging from: (1) a better global spatial coverage (i.e. Southern Ocean), (2) a deeper reference level (1000 m versus 700 m), (3) the use of measured S profiles (versus climatological S), (4) the separation of the different temporal scales (low frequency versus mesoscale) and (5) the study of seasonal to interannual SLA minus DHA signals.

Data and method
Correlation between Argo and altimeter observations
Removing the effect of seasonal signals
Influence of the vertical structure of the ocean
SLA minus DHA signals: from seasonal to interannual
Findings
Conclusions
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