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 highly integrated with the MITgcm. They are both subjected to regular, automated regression tests. The modeling framework includes sets of global conformal grids, a global model setup, implementations of model-data constraints and adjustable control parameters, an interface to algorithmic differentiation, as well as a grid-independent, fully capable Matlab toolbox. The reference ECCO v4 solution is a dynamically consistent ocean state estimate (ECCO-Production, release 1) without un-identified 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 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.

Change of the Global Ocean Vertical Heat Transport over 1993–2010

“…Previous publications have demonstrated that the estimates fit altimetry , SST (Buckley et al 2014), subsurface hydrography data (Forget et al 2015a), and the Atlantic meridional overturning circulation (Wunsch and Heimbach 2013b) at or close to the specified noise level. Many quantities (e.g., isopycnal mixing) for which no corresponding observations are available have been analyzed in some detail and found to be at least physically plausible (Forget et al 2015b).…”

“…In this study, we use the ECCO v4r1 estimate (Forget et al 2015a) and analyze the net air-sea heat flux Q net and the ocean vertical heat flux H y . The ECCO v4r1 estimate covers the period 1992-2011, when altimetry measurements of high precision, quasi-global coverage of ocean in situ measurements, mainly Argo temperature and salinity profiles, and the WOCE hydrographic program and the follow-on hydrography, are available.…”

“…A priori forcing fields of ECCO v4r1 are from the ERA-Interim (Dee et al 2011). Surface atmospheric fields (temperature, humidity, downward radiation, precipitation, and wind stress) are control parameters and are adjusted using the adjoint method (Forget et al 2015a). Latent, sensible, and upward radiative components of Q net are computed using the bulk formulas of Large and Yeager (2004) and the adjusted near-surface atmospheric fields.…”

Recent Wind-Driven Variability in Atlantic Water Mass Distribution and Meridional Overturning Circulation

“…The role of the atmosphere is often divided between the long-term impact of buoyancy forcing due to air-sea fluxes of heat and freshwater and the action of winds on the sea surface (Polo et al 2014;Forget and Ponte 2015). The ocean circulation can adjust to the latter on short time scales (hours to months) through barotropic dynamics (Willebrand et al 1980;Andres et al 2011Andres et al , 2012) and on longer time scales (years to decades) through various baroclinic modes (Anderson and Gill 1975;Williams et al 2014;Forget and Ponte 2015).…”

Recent Advances in Japanese Fisheries Science in the Kuroshio-Oyashio Region through Development of the FRA-ROMS Ocean Forecast System: Overview of the Reproducibility of Reanalysis Products

“…Progress in this area is attributable primarily to the Global Ocean Data Assimilation Experiment project, which, crucially, supports near-real-time collection, processing, and management of oceanographic data [6] [7]. In addition, several state-of-the-art community ocean models, which can be coupled with data assimilation schemes, have been developed as open-source code (e.g., ROMS [8] and ECCO [9]) and have contributed to making ocean nowcasting and forecasting relatively easy. Consequently, physical oceanographic studies have made rapid progress in the areas of ocean dynamics and predictability.…”