Abstract

1. Variability in the north Atlantic region Use of global ocean reanalyses for reconstructing sea level variability patterns over the last 40 yearsmethods, results and limitations Philippe Rogel , Clément Ubelmann, Anthony T. Weaver,Nicolas Daget Global Change and Climate Modelling Team, CERFACS, Toulouse, France rogel@cerfacs.fr http://www.cerfacs.fr/globc 15 years of progress in Radar Altimetry , Venice, 2006 Abstract A 40-year reanalysis (1962-2001) of the ocean circulation, carried out in the framework of the European ENACT project, has been used to investigate how sea level variability can be reconstructed. This reanalysis has been obtained by the assimilation of historical in situ temperature measurements into a global low-resolution version of the OPA ocean GCM. The observational data set is the quality controlled in situ data gathered for the ENACT project by the Metoffice (Ingleby and Huddelston, 2005). The assimilation scheme is the 3D-Variational scheme described e.g. in Ricci et al. (2005), with a full multivariate background error covariance term, which enables the correction of all model variables at each 10-day analysis window. The model’s hypothesis of a local free surface, but assuming a constant total volume of the ocean, does not allow to investigate global mean sea level variations over the period, but only regional patterns of variability. It furthermore imposes rigorous constraints for any comparison to observations such as altimetry or tide gauges, and, theoretically, for the computation of innovations in the context of data assimilation. Therefore, the protocol for a rigorous comparison is exposed. This validation shows that, though the best agreement is in the tropical regions, low frequency signals at the midlatitudes in sufficient agreement to be analysed over long periods. Over such a long period, the assimilation system causes long term drifts which have regional signatures, mostly due to the act that salinity variations are not sufficiently constrained by the assimilation scheme. Fortunately, the more-than-a-decade record of altimetry helps identifying them using a statistical method. An application is shown in the North Atlantic region, where the drift is a 15-year regular change of the east-west sea level gradient. Finally, corrected sea level anomalies are analysed in this region through Empirical Orthogonal Functions. The dominant mode is close to the tripole observed in winter SST anomalies. Link with winter North Atlantic Oscillation forcing is evidenced. • The 3D-Var global ocean analysis system • System developed for OPA/NEMO global, low resolution (2°x1.5°) ocean general circulation model (Weaver et al., 2003, Vialard et al., 2003). • 3D-Var FGAT formulation, allows for exact innovation computation • Jb modelling (Weaver et al., 2006): • Flow dependent error variance • Multivariate balanced/unbalanced formalism (Derber and Bouttier) • Flow dependent multivariate T-S preservation scheme • Flow dependent density/currents scheme (extended geostrophy) • Allows for assimilation of altimeter data • Interfaced with the ENACT in situ data based (Ingleby and Huddelston, 2005) containing historical temperature and salinity data since 1958. • The development of the system, has been conducted under the EU-FP5 ENACT project • This system is also used to produce ocean initial states for seasonal climate prediction (e.g. Rogel et al., 2005) • The analyses • Reanalyses have been done following the ENACT common protocol • “Control” (no assimilation) and 3D-Var experiments have been carried out using the ERA-40 winds and fluxes. • In situ temperature profiles only have been assimilated, over the 1962-2001 period • Reference sea level observations are the CLS monthly gridded maps produced for the ENAC project • The sea level variations • By construction, the model does not represent the global mean sea level variations. Therefore, a rigorous comparison method has been set up. • Several sea level estimates have been inferred from the ocean anlyses (free surface elevation, dynamic height including salinity variations or not). • Mean sea level variations between 65N and 65S have been removed in order to rigorously compare model and observations. • Therefore: only relative regional sea level variations can be validated. The global mean sea level rise problem can not be addressed here. Global comparison and validation Regional trends of sea level variations in observations and reanalysis Global validation of the sea level estimates T/P Correlation between « Control » free surface elevation and T/P observtions Correlation between « Control » dynamic height and T/P observtions cm/month • Sea level variations are globally better reproduced in the tropical areas, and in the eastern parts of the subtropical basins. • Dynamic height variations are more reliable than free surface elevation. • Assimilation slightly degrades sea level variations, mostly due to a degradation of salinity • For multi decadal variations, dynamic height corrected from salinity is further used. 3D-Var Correlation between « 3D-Var » dynamic height and T/P observtions Link with NAO • Maximum correlation for a 3 month lead of the atmosphere. • Significant correlations persist more than one year in the control experiment. • This is not the case in the 3D-Var. Sea level correlation with Winter NAO at 0, 3 and 10 month lags in the control case. Winter NAO index, Principal component of sea level for Control 1st mode and for 3D-VAR 1st mode • Conclusions • Though the quality of ocean reanalysis of temperature profiles over a 40 year period is still subject to improvements, it is possible to use them for the analysis of sea level variations. • Limitations are caused by salinity drifts, that can be corrected with the assimilation of salinity data, even if they are sparse. • The regional analysis of sea level variations linked with NAO raises some questions about the model reaction in free mode. 3 months 12 months Lead/lag correlation between Winter NAO index and the above indices (solid line for control, dashed line for 3D-Var). The NAO leads sea level for positive lags. References • J. Vialard, A. T. Weaver, D. L. T. Anderson and P. Delecluse, 2003: Three- and four-dimensional variational assimilation with a general circulation model of the tropical Pacific ocean. Part 2: Physical validation. Monthly Weather Review, 1379-1395. • Weaver, A. T. J. Vialard, D. L. T. Anderson, 2003: Three- and four-dimensional variational assimilation with a general circulation model of the tropical Pacific ocean. Part 1: Formulation, internal diagnostics and consistency checks. Monthly Weather Review, 131,1360-1378. • Weaver, A.T., C. Deletel, E. Machu and S. Ricci. A multivariate balance operator for variational ocean data assimilation. Submitted to Q.J.R. Meteorol.Soc., 2006. • Ingleby, B, and M. Huddleston, Quality control of ocean temperature and salinity profiles – historical and real-time data. To appear in J. Mar. Sys. • Ricci, S., A.T. Weaver, J. Vialard, and P. Rogel, 2005: Incorporating state-dependent temperature-salinity constraints in the background-error covariance of variational ocean data assimilation. Mon. Wea. Rev., 133, 317-338. • Rogel, P., A. T. Weaver, N. Daget, S. Ricci and E. Machu, Ensembles of global ocean analyses for seasonal climate prediction: impact of temperature assimilation, Tellus, in press, 2005.