A large-scale view of oceanic variability from 2007 to 2015 in the global high resolution monitoring and forecasting system at Mercator Océan
|Author(s)||Gasparin Florent1, Greiner Eric2, Lellouche Jean-Michel1, Legalloudec Olivier1, Garric Gilles1, Drillet Yann1, Bourdalle-Badie Romain1, Le Traon Pierre-Yves1, 3, Remy Elisabeth1, Drevillon Marie1|
|Affiliation(s)||1 : Mercator Ocean Int, 10 Rue Hermes, F-31520 Ramonville St Agne, France.
2 : CLS, 11 Rue Hermes, F-31520 Ramonville St Agne, France.
3 : Ifremer, F-29280 Plouzane, France.
|Source||Journal Of Marine Systems (0924-7963) (Elsevier Science Bv), 2018-11 , Vol. 187 , P. 260-276|
|WOS© Times Cited||12|
|Keyword(s)||Global ocean monitoring and forecasting system, Climate variability, System evaluation/qualification, Global ocean observing system|
The global high resolution monitoring and forecasting system PSY4 at Mercator Océan, initialized in October 2006, has achieved 11 years of global ocean state estimation. Based on the NEMO global 1/12° configuration, PSY4 includes data assimilation of satellite and multi-instrument in situ observations. In parallel to this monitoring system, a twin-free simulation (with no assimilation) has been performed for the period 2007-2015. In this study, monthly-averaged fields of both ocean state estimates are compared with observation products for the period 2007-2015, to examine the consistency of PSY4 fields with related observations for representing large-scale variability and to provide a baseline that is mainly focused on in situ comparisons for validation/qualification of on-going system developments. Observations play a major role in correctly positioning the main energetic structures, both in space and time. In addition, data assimilation appears to overcome the other deficiencies of models by reducing SST bias in upwelling regions and by increasing the thermocline gradient in the tropics. Generally, the amplitude of the total-resolved variability in both PSY4 estimates is consistent with observation data sets. Annual cycle and longer-term variability in temperature, salinity and sea surface height are significantly improved with data assimilation, but some progress is still needed to better represent the amplitude of changes of ocean heat and freshwater contents on long timescales. Finally, the PSY4 system’s ability to capture the large scale variability is further investigated by using as a case study the northward pathways of El Niño anomalies in the tropical North Pacific in 2014 and 2015 in order to illustrate how such systems can be used to answer relevant scientific questions.