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Authors: Balmaseda, M. A.* 
Hernandez, F.* 
Storto, A.* 
Palmer, M. D.* 
Alves, O.* 
Shi, L.* 
Smith, G. C.* 
Toyoda, T.* 
Valdivieso, M.* 
Barnier, B.* 
Behringer, D.* 
Boyer, T.* 
Chang, Y-S.* 
Chepurin, G. A.* 
Ferry, N.* 
Forget, G.* 
Fujii, Y.* 
Good, S.* 
Guinehut, S.* 
Haines, K.* 
Ishikawa, Y.* 
Keeley, S.* 
Köhl, A.* 
Lee, T.* 
Martin, M.* 
Masina, S.* 
Masuda, S.* 
Meyssignac, B.* 
Mogensen, K.* 
Parent, L.* 
Peterson, K. A.* 
Tang, Y. M.* 
Yin, Y.* 
Vernieres, G.* 
Wang, X.* 
Waters, J.* 
Wedd, R.* 
Wang, O.* 
Xue, Y.* 
Chevallier, M.* 
Lemieux, J-F.* 
Dupont, F.* 
Kuragano, T.* 
Kamachi, M.* 
Awaji, T.* 
Caltabiano, A.* 
Wilmer - Becker, K.* 
Gaillard, F.* 
Title: The Ocean Reanalyses Intercom parison Project (ORA - IP)
Journal: Journal of Operational Oceanography 
Series/Report no.: Sup 1/8 (2015)
Issue Date: 2015
DOI: 10.1080/1755876X.2015.1022329
Keywords: Global ocean–sea-ice modelling
Ocean model comparisons
multi-analysis ensemble
Ocean climate
Subject Classification03. Hydrosphere::03.01. General::03.01.04. Ocean data assimilation and reanalysis 
Abstract: Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth,upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea-ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.
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