. 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.

/pdf/ecco-version-4-an-integrated-framework-for-non-linear-4sj9o3ygtx.pdf

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

United States. National Aeronautics and Space Administration. Physical Oceanography Program

This paper describes version 3 of the Simple Ocean Data Assimilation (SODA3) ocean reanalysis with enhancements to model resolution, observation, and forcing datasets, and the addition of a...

SODA3: A New Ocean Climate Reanalysis

The current Earth's energy imbalance (EEI) is mostly caused by human activity, and is driving global warming. The absolute value of EEI represents the most fundamental metric defining the status of global climate change, and will be more useful than using global surface temperature. EEI can best be estimated from changes in ocean heat content, complemented by radiation measurements from space. Sustained observations from the Argo array of autonomous profiling floats and further development of the ocean observing system to sample the deep ocean, marginal seas and sea ice regions are crucial to refining future estimates of EEI. Combining multiple measurements in an optimal way holds considerable promise for estimating EEI and thus assessing the status of global climate change, improving climate syntheses and models, and testing the effectiveness of mitigation actions. Progress can be achieved with a concerted international effort.

/pdf/an-imperative-to-monitor-earth-s-energy-imbalance-12odnlbow1.pdf

An imperative to monitor Earth's energy imbalance

. The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description
of the OCEAN5 system, with the focus on upgrades of system components with
respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5
is the ensemble generation strategy that includes perturbation of initial
conditions and a generic perturbation scheme for observations and forcing
fields. Other upgrades include revisions to the a priori bias correction
scheme, observation quality control and assimilation method for sea-level
anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice
state is the ORAS5 reanalysis, which includes five ensemble members and covers
the period from 1979 onwards. Updated versions of observation data sets are
used in ORAS5 production, with special attention devoted to the consistency
of sea surface temperature (SST) and sea-ice observations. Assessment of
ORAS5 through sensitivity experiments suggests that all system components
contribute to an improved fit to observation in reanalyses, with the most
prominent contribution from direct assimilation of ocean in situ
observations. Results of observing system experiments further suggest that
the Argo float is the most influential observation type in our data assimilation
system. Assessment of ORAS5 has also been carried out for several key ocean
state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and
variability in terms of SST and sea level, mostly due to increased model
resolution and updates in assimilated observation data sets. In spite of the
improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension
regions which are possibly associated with misrepresentation of front
positions. Overall, the SST and sea-ice uncertainties estimated using five
ORAS5 ensemble members have spatial patterns consistent with those of
analysis error. The ensemble spread of sea ice is commensurable with the
sea-ice analysis error. On the contrary, the ensemble spread is
under-dispersive for SST.

/pdf/the-ecmwf-operational-ensemble-reanalysis-analysis-system-57rbutn32u.pdf

The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment

Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.

/pdf/the-ocean-reanalyses-intercomparison-project-ora-ip-3bmmd8spp0.pdf

The Ocean Reanalyses Intercomparison Project (ORA-IP)


6th WGNE workshop on systematic errors in weather and climate models What: Scientists, ranging from early career to highly experienced, involved in the development of weather and climate models and in the diagnosis of model errors, held an international workshop to discuss the nature, causes and remedies of systematic errors across timescales and across Earth system modeling components. When: 31 Oct - 04 Nov 2022 Where: Reading, UK and online

https://journals.ametsoc.org/downloadpdf/journals/bams/aop/BAMS-D-23-0102.1/BAMS-D-23-0102.1.pdf

A. Caltabiano

Papers

The Ocean Reanalyses Intercomparison Project (ORA-IP)

Systematic Errors in Weather and Climate Models: Challenges and Opportunities in Complex Coupled Modeling Systems