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Global ocean mean salinity S is a key indicator of the Earth's hydrological cycle and the exchanges of freshwater between land and ocean, but its determination remains a challenge. Aside from traditional methods based on gridded salinity fields derived from in situ measurements, we explore estimates of S based on liquid freshwater changes derived from space gravimetry data corrected for sea ice effects. For the 2005-2019 period analyzed, the different S series show little consistency in seasonal, interannual, and long-term variability. In situ estimates show sensitivity to choice of product and unrealistic variations. A suspiciously large rise in S since ~2015 is enough to measurably affect halosteric sea level estimates and can explain recent discrepancies in the global mean sea level budget. Gravimetrybased S estimates are more realistic, inherently consistent with estimated freshwater contributions to global mean sea level, and provide a way to calibrate the in situ estimates. [ABSTRACT FROM AUTHOR]

Earth rotation studies require accurate knowledge of the global oceanic velocity and mass fields, for proper accounting of ocean angular momentum (OAM) effects on the planetary budget. We analyse a new OAM series (1992–2015) based on the solution of a global general circulation model constrained to most existing ocean data. The impact of the data-constrained optimization on OAM is substantial, and particularly essential for calculating effects of global mean ocean mass changes, which can be important for determining annual cycles and long term trends in OAM. The contributions of sea ice to OAM variations, also estimated, are found to be negligible. Uncertainties in OAM series are assessed by comparison with other available estimates. Results indicate low signal-to-noise ratios for all the analysed OAM series. Comparisons with geodetic, atmospheric and hydrologic data, in the context of the planetary angular momentum budget, point to the continued need for improvements in some or all of the series. Possible paths are offered for producing better OAM estimates in the future. [ABSTRACT FROM AUTHOR]

Abstract: Global mean sea surface temperature ( T ¯) is a variable of primary interest in studies of climate variability and change. The temporal evolution of T ¯ can be influenced by surface heat fluxes ( F ¯) and by diffusion ( D ¯) and advection ( A ¯) processes internal to the ocean, but quantifying the contribution of these different factors from data alone is prone to substantial uncertainties. Here we derive a closed T ¯ budget for the period 1993–2015 based on a global ocean state estimate, which is an exact solution of a general circulation model constrained to most extant ocean observations through advanced optimization methods. The estimated average temperature of the top (10‐m thick) level in the model, taken to represent T ¯, shows relatively small variability at most time scales compared to F ¯, D ¯, or A ¯, reflecting the tendency for largely balancing effects from all the latter terms. The seasonal cycle in T ¯ is mostly determined by small imbalances between F ¯ and D ¯, with negligible contributions from A ¯. While D ¯ seems to simply damp F ¯ at the annual period, a different dynamical role for D ¯ at semiannual period is suggested by it being larger than F ¯. At periods longer than annual, A ¯ contributes importantly to T ¯ variability, pointing to the direct influence of the variable ocean circulation on T ¯ and mean surface climate. [ABSTRACT FROM AUTHOR]

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 gridindependent, 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]