The PO.DAAC is pleased to announce the public release of the ancillary data for the (Estimating the Circulation and Climate of the Ocean) ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. ECCO ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states (ECCO Consortium, 2021). More information regarding the ECCO project is available from PO.DAAC’s mission webpage.
The ECCO V4r4 ancillary data are intended for expert users who need the set of files required to reproduce ECCO V4r4. These include documentation files, files required to initialize the model, forcing fields, binary input grid files for the "Lat-Lon-Cap" 90 (LLC90) native model grid (Forget et al., 2015), observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. The dataset is provided as eight tar files, with each tar file for a particular category listed above. The ancillary dataset includes a complete set of input files needed to reproduce the ECCO V4r4 state estimate. The ancillary files, such as the uncertainties for the controls and the control adjustments themselves, are useful to analyze the ECCO V4r4 product.
Citation of the ancillary data will be inclusive of the respective DOI (see below) using the following citation template: ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M.. 2021. ECCO Ancillary Data (Version 4 Release 4). PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at 10.5067/ECCL4-ANC44.
ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. (2021, February 10). Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). https://doi.org/10.5281/zenodo.3765928
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M. & Wunsch, C. (2015). ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev., 8, 3071–3104. https://doi.org/10.5194/gmd-8-3071-2015