10.5281/zenodo.4533349
https://zenodo.org/records/4533349
oai:zenodo.org:4533349
ECCO Consortium
ECCO Consortium
Fukumori, Ichiro
Ichiro
Fukumori
Jet Propulsion Laboratory, California Institute of Technology
Wang, Ou
Ou
Wang
Jet Propulsion Laboratory, California Institute of Technology
Fenty, Ian
Ian
Fenty
0000-0001-6662-6346
Jet Propulsion Laboratory, California Institute of Technology
Forget, Gael
Gael
Forget
Massachusetts Institute of Technology
Heimbach, Patrick
Patrick
Heimbach
University of Texas at Austin
Ponte, Rui M.
Rui M.
Ponte
Atmospheric and Environmental Research, Inc.
Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate, Version 4 Release 4
Zenodo
2021
ECCO
adjoint
estimation
oceanography
2021-02-10
10.5281/zenodo.3765928
https://zenodo.org/communities/ecco
4 Release 4
Creative Commons Attribution 4.0 International
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.