ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation

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.

On the Benefit of Current and Future ALPS Data for Improving Arctic Coupled Ocean-Sea Ice State Estimation

“…A state estimation framework that combines observations with a state-of-the-art numerical model, while strictly adhering to the underlying conservation equations, can help infer the mechanisms responsible for changing heat, salt, and freshwater budgets. Currently available ALPS data have been heavily utilized within the ECCO framework to improve estimates of the time-evolving global ocean-sea ice state on decadal time scales (Wunsch and Heimbach, 2013;Forget et al, 2015a).…”

“…One such smoother-based framework is that developed within the Estimation of the Circulation and Climate of the Ocean (ECCO) consortium (Stammer et al, 2002;Heimbach, 2007, 2013;Forget et al, 2015a). In this paper, we briefly introduce the ECCO framework as a computational tool for constructing a dynamically evolving Arctic ocean-sea ice state estimate through data-model synthesis.…”

Quantifying the drivers of ocean‐atmosphere CO2 fluxes

“…Advective and eddy-diffusive phosphate fluxes are easily obtained from ocean models and may be reconstructed offline (with some introduced error) if the flow field and phosphate concentration is known. Estimates may also be based on climatological distribution of phosphate (e.g., the World Ocean Atlas [Garcia et al, 2014]) with flow field and mixing parameters based on ocean state estimates [Wunsch and Heimbach, 2007;Forget, 2010;Forget et al, 2015]. We note that nitrate might equally well be the basis of the biological flux divergence estimate, depending on the currency of the model or data available.…”

El Niño, La Niña, and the global sea level budget

“…Any more definitive diagnosis of the heat budgets would require a more advanced approach. For example, future studies could use an ocean state estimate covering the altimetric era (e.g., Forget et al, 2015), not only to investi- gate a longer time period and corroborate or refute the purely observational results presented here, but also to better understand the physical processes contributing to the global and regional steric changes (cf. Ponte, 2011, 2014).…”