Abstract
Abstract. This paper presents the ECCO v4 non-linear inverse modeling framework and its baseline solution for the evolving ocean state over the period 1992–2011. Both components are publicly available and subjected to regular, automated regression tests. The modeling framework includes sets of global conformal grids, a global model setup, implementations of data constraints and control parameters, an interface to algorithmic differentiation, as well as a grid-independent, fully capable Matlab toolbox. The baseline ECCO v4 solution is a dynamically consistent ocean state estimate without unidentified sources of heat and buoyancy, which any interested user will be able to reproduce accurately. The solution is an acceptable fit to most data and has been found to be physically plausible in many respects, as documented here and in related publications. Users are being provided with capabilities to assess model–data misfits for themselves. The synergy between modeling and data synthesis is asserted through the joint presentation of the modeling framework and the state estimate. In particular, the inverse estimate of parameterized physics was instrumental in improving the fit to the observed hydrography, and becomes an integral part of the ocean model setup available for general use. More generally, a first assessment of the relative importance of external, parametric and structural model errors is presented. Parametric and external model uncertainties appear to be of comparable importance and dominate over structural model uncertainty. The results generally underline the importance of including turbulent transport parameters in the inverse problem.
Highlights
The history of inverse modeling in oceanography goes back at least 4 decades
This paper emphasizes the synergy between ocean modeling and data analysis
The synergy of ocean modeling and data analysis is further becoming a reality as a growing community engages in ocean state estimation, which in essence is the hybridization of ocean modeling and data analysis
Summary
The history of inverse modeling in oceanography goes back at least 4 decades (see Wunsch, 2006, for a general presentation). In the context of the Estimating the Circulation and Climate of the Ocean (ECCO) project, the MITgcm non-linear inverse modeling framework (using the adjoint method and algorithmic differentiation) has become a common tool for data synthesis, applied by many investigators to derive ocean state estimates (Stammer et al, 2004; Wunsch et al, 2007; Köhl et al, 2007; Köhl and Stammer, 2008; Forget et al, 2008b; Wunsch and Heimbach, 2009; Hoteit et al, 2009; Forget, 2010; Mazloff et al, 2010; Köhl et al, 2012; Speer and Forget, 2013; Köhl, 2014; Losch et al, 2014; Dail and Wunsch, 2014). The baseline ECCO v4 solution (the ECCO v4, release 1 state estimate) is the subject of Sect. 5, which is followed by conclusions and perspectives (Sect. 6)
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