Description: 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 indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

Description: Published

Description: 813–841

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: The interannual-decadal variability of the wintertime mixed layer depths (MLDs) over the North Pacific is investigated from an empirical orthogonal function (EOF) analysis of an ensemble of global ocean reanalyses. The first leading EOF mode represents the interannual MLD anomalies centered in the eastern part of the central mode water formation region in phase opposition with those in the eastern subtropics and the central Alaskan Gyre. This first EOF mode is highly correlated with the Pacific decadal oscillation index on both the interannual and decadal time scales. The second leading EOF mode represents the MLD variability in the subtropical mode water (STMW) formation region and has a good correlation with the wintertime West Pacific (WP) index with time lag of 3 years, suggesting the importance of the oceanic dynamical response to the change in the surface wind field associated with the meridional shifts of the Aleutian Low. The above MLD variabilities are in basic agreement with previous observational and modeling findings. Moreover the reanalysis ensemble provides uncertainty estimates. The interannual MLD anomalies in the first and second EOF modes are consistently represented by the individual reanalyses and the amplitudes of the variabilities generally exceed the ensemble spread of the reanalyses. Besides, the resulting MLD variability indices, spanning the 1948–2012 period, should be helpful for characterizing the North Pacific climate variability. In particular, a 6-year oscillation including the WP teleconnection pattern in the atmosphere and the oceanic MLD variability in the STMW formation region is first detected.

Description: Published

Description: 891–907

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10–20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5–7 (14–16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m−3 is used for the MLD estimation. Using the larger criterion (0.125 kg m−3) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.

Description: Published

Description: 753–773

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124, (2019): 9141-9170, doi: 10.1029/2019JC015210.

Description: The observational network around the North Atlantic has improved significantly over the last few decades with subsurface profiling floats and satellite observations and the recent efforts to monitor the Atlantic Meridional Overturning Circulation (AMOC). These have shown decadal time scale changes across the North Atlantic including in heat content, heat transport, and the circulation. However, there are still significant gaps in the observational coverage. Ocean reanalyses integrate the observations with a dynamically consistent ocean model and can be used to understand the observed changes. However, the ability of the reanalyses to represent the dynamics must also be assessed. We use an ensemble of global ocean reanalyses to examine the time mean state and interannual‐decadal variability of the North Atlantic ocean since 1993. We assess how well the reanalyses are able to capture processes and whether any understanding can be gained. In particular, we examine aspects of the circulation including convection, AMOC and gyre strengths, and transports. We find that reanalyses show some consistency, in particular showing a weakening of the subpolar gyre and AMOC at 50°N from the mid‐1990s until at least 2009 (related to decadal variability in previous studies), a strengthening and then weakening of the AMOC at 26.5°N since 2000, and impacts of circulation changes on transports. These results agree with model studies and the AMOC observations at 26.5°N since 2005. We also see less spread across the ensemble in AMOC strength and mixed layer depth, suggesting improvements as the observational coverage has improved.

Description: This work was initiated through the EU COST‐EOS‐1402 project which supported the development of this paper by funding project meetings, both in person and virtual. We would like to thank Aida Azcarate for organizing the funding for the meetings and would like to thank Martha Buckley, Gokhan Danabasoglu, and Simon Josey for useful discussions. Jackson, Storto and Zuo were partially funded, by the Copernicus Marine Environment Monitoring Service (CMEMS: 23‐GLO‐RAN) and Zuo was partially funded by the Copernicus Climate Change Service. Jackson was also partially funded by the joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). Haines and Robson acknowledge funding under the NERC RAPID projects RAMOC and DYNAMOC (NE/M005127/1) respectively, and Robson also acknowledges funding from the ACSIS project. Mignac was supported for PhD scholarship by the CAPES Foundation, Ministry of Education of Brazil (Proc. BEX 1386/15‐8). Forget acknowledges support from the Simons Foundation (549931) and the NASA IDS program (6937342). Work by Piecuch was carried out under the ECCO project, funded by the NASA Physical Oceanography, Cryospheric Science, and Modeling, Analysis and Prediction programs, and supported by the Independent Research and Development Program at Woods Hole Oceanographic Institution. Wilson was funded by the NERC UK‐OSNAP project (NE/K010875.1) as part of the international OSNAP program. NorCPM‐v1 reanalysis was cofunded by the Center for Climate Dynamics at the Bjerknes Center, the Norwegian Research Council under the EPOCASA (229774/E10) and SFE (270733) research projects, the NordForsk under the Nordic Centre of Excellence (ARCPATH, 76654), and the Trond Mohn Foundation under the project BFS2018TMT01. NorCPM‐v1 reanalysis received a grant for computer time from the Norwegian Program for supercomputer (NOTUR2, project NN9039K) and a storage grant (NORSTORE, NS9039K). Data for the figures are available to download (from https://doi.org/10.5281/zenodo.2598509). Data from some reanalysis products are available to download (from http://marine.copernicus.eu/services-portfolio/access-to-products/) under product names GLOBAL_REANALYSIS_PHY_001_025 (GLORYS2v4), GLOBAL_REANALYSIS_PHY_001_026 (C‐GLORSv7, GLORYS2v4, GloSea5 and ORAS5) and GLOBAL_REANALYSIS_PHY_001_030 (GLORYS12V1).

Description: 2020-05-06