Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate, Version 4 Release 4
This note provides a brief synopsis of ECCO Version 4 Release 4 (R4), an updated edition to the global ocean state estimate described by Forget et al. (2015b, 2016) and Fukumori et al. (2017). Release 4 is available at https://ecco.jpl.nasa.gov/drive/files/Version4/Release4. As of this writing, Vers...
Published in: | Geochemistry, Geophysics, Geosystems |
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Main Authors: | , , , , , , |
Format: | Report |
Language: | unknown |
Published: |
2020
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Subjects: | |
Online Access: | https://zenodo.org/record/3765929 https://doi.org/10.5281/zenodo.3765929 |
Summary: | This note provides a brief synopsis of ECCO Version 4 Release 4 (R4), an updated edition to the global ocean state estimate described by Forget et al. (2015b, 2016) and Fukumori et al. (2017). Release 4 is available at https://ecco.jpl.nasa.gov/drive/files/Version4/Release4. As of this writing, Version 4 represents the latest ocean state estimate of the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) (Wunsch et al., 2009; Wunsch and Heimbach, 2013) that synthesizes nearly all modern observations with an ocean circulation model (MITgcm, originally described by Marshall et al., 1997) into coherent, physically consistent descriptions of the ocean’s time-evolving state covering the era of satellite altimetry. Among its characteristics, Version 4 (Forget et al., 2015b; Release 1 [R1]) is the first multidecadal ECCO estimate (1992-2011) that is truly global, including the Arctic Ocean. Unlike previous versions, the model uses a nonlinear free surface formulation and real freshwater flux boundary condition, permitting a more accurate simulation of sea level change. In addition to estimating forcing and initial conditions as done in earlier analyses, the Version 4 estimate also adjusts the model’s mixing parameters that enables an improved fit to observations (Forget et al., 2015a). The Version 4 synthesis also incorporates a diffusion operator in evaluating model-data misfits (Forget and Ponte, 2015) and controls (Weaver and Courtier, 2001), accounting for some of the spatial correlation that exist among these elements. |
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