An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

• Published in: Climate Dynamics
• Main Authors: Masina, Simona, Storto, Andrea, Ferry, Nicolas, Valdivieso, Maria, Haines, Keith, Balmaseda, Magdalena, Zuo, Hao, Drevillon, Marie, Parent, Laurent
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer 2017
• Subjects: Sea ice
• Online Access: https://centaur.reading.ac.uk/52360/; https://centaur.reading.ac.uk/52360/1/art%253A10.1007%252Fs00382-015-2728-5.pdf; http://link.springer.com/article/10.1007%2Fs00382-015-2728-5

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global ...